

"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       AudioLDMPipeline
					lowercase__				 =       TEXT_TO_AUDIO_PARAMS
					lowercase__				 =       TEXT_TO_AUDIO_BATCH_PARAMS
					lowercase__				 =       frozenset(
					    [
					        "num_inference_steps",
					        "num_waveforms_per_prompt",
					        "generator",
					        "latents",
					        "output_type",
					        "return_dict",
					        "callback",
					        "callback_steps",
					    ]       )







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						torch.manual_seed(0		)
						lowerCAmelCase__	:						List[str]  				=	UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)   ,layers_per_block=2   ,sample_size=3_2   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=(3_2, 6_4)   ,class_embed_type='''simple_projection'''   ,projection_class_embeddings_input_dim=3_2   ,class_embeddings_concat=lowercase_   ,)
						lowerCAmelCase__	:						List[Any]  				=	DDIMScheduler(
						    beta_start=0.0_0085   ,beta_end=0.012   ,beta_schedule='''scaled_linear'''   ,clip_sample=lowercase_   ,set_alpha_to_one=lowercase_   ,)
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Any  				=	AutoencoderKL(
						    block_out_channels=[3_2, 6_4]   ,in_channels=1   ,out_channels=1   ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,latent_channels=4   ,)
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Dict  				=	ClapTextConfig(
						    bos_token_id=0   ,eos_token_id=2   ,hidden_size=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,projection_dim=3_2   ,)
						lowerCAmelCase__	:						int  				=	ClapTextModelWithProjection(lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''   ,model_max_length=7_7		)

						lowerCAmelCase__	:						Union[str, Any]  				=	SpeechTaHifiGanConfig(
						    model_in_dim=8   ,sampling_rate=1_6_0_0_0   ,upsample_initial_channel=1_6   ,upsample_rates=[2, 2]   ,upsample_kernel_sizes=[4, 4]   ,resblock_kernel_sizes=[3, 7]   ,resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]   ,normalize_before=lowercase_   ,)

						lowerCAmelCase__	:						Any  				=	SpeechTaHifiGan(lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	{
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''vocoder''': vocoder,
						}
						return components







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Dict   ,lowercase_			:				Optional[Any]=0		):
						if str(lowercase_		).startswith('''mps'''		):
							lowerCAmelCase__	:						Optional[Any]  				=	torch.manual_seed(lowercase_		)
						else:
							lowerCAmelCase__	:						Union[str, Any]  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	{
						    '''prompt''': '''A hammer hitting a wooden surface''',
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 6.0,
						}
						return inputs







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						int  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Optional[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						str  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 2_5_6

						lowerCAmelCase__	:						str  				=	audio[:1_0]
						lowerCAmelCase__	:						str  				=	np.array(
						    [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]		)

						assert np.abs(audio_slice - expected_slice		).max() < 1E-2







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Any  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						int  				=	audioldm_pipe.to(lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	3 * [inputs['''prompt''']]

						# forward
						lowerCAmelCase__	:						int  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	output.audios[0]

						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						str  				=	3 * [inputs.pop('''prompt'''		)]

						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.tokenizer(
						    lowercase_   ,padding='''max_length'''   ,max_length=audioldm_pipe.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors='''pt'''   ,)
						lowerCAmelCase__	:						Tuple  				=	text_inputs['''input_ids'''].to(lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.text_encoder(
						    lowercase_   ,)
						lowerCAmelCase__	:						Tuple  				=	prompt_embeds.text_embeds
						# additional L_2 normalization over each hidden-state
						lowerCAmelCase__	:						Tuple  				=	F.normalize(lowercase_   ,dim=-1		)

						lowerCAmelCase__	:						List[Any]  				=	prompt_embeds

						# forward
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						Any  				=	output.audios[0]

						assert np.abs(audio_a - audio_a		).max() < 1E-2







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						List[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe.to(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						int  				=	3 * ['''this is a negative prompt''']
						lowerCAmelCase__	:						Any  				=	negative_prompt
						lowerCAmelCase__	:						int  				=	3 * [inputs['''prompt''']]

						# forward
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	output.audios[0]

						lowerCAmelCase__	:						Optional[Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	3 * [inputs.pop('''prompt'''		)]

						lowerCAmelCase__	:						Optional[Any]  				=	[]
						for p in [prompt, negative_prompt]:
							lowerCAmelCase__	:						int  				=	audioldm_pipe.tokenizer(
							    lowercase_   ,padding='''max_length'''   ,max_length=audioldm_pipe.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors='''pt'''   ,)
							lowerCAmelCase__	:						Optional[Any]  				=	text_inputs['''input_ids'''].to(lowercase_		)

							lowerCAmelCase__	:						Dict  				=	audioldm_pipe.text_encoder(
							    lowercase_   ,)
							lowerCAmelCase__	:						Dict  				=	text_embeds.text_embeds
							# additional L_2 normalization over each hidden-state
							lowerCAmelCase__	:						Any  				=	F.normalize(lowercase_   ,dim=-1		)

							embeds.append(lowercase_		)

						lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	embeds

						# forward
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	output.audios[0]

						assert np.abs(audio_a - audio_a		).max() < 1E-2







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Dict  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Optional[int]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Union[str, Any]  				=	PNDMScheduler(skip_prk_steps=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Dict  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	'''egg cracking'''
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe(**lowercase_   ,negative_prompt=lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 2_5_6

						lowerCAmelCase__	:						Any  				=	audio[:1_0]
						lowerCAmelCase__	:						List[Any]  				=	np.array(
						    [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]		)

						assert np.abs(audio_slice - expected_slice		).max() < 1E-2







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						str  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_components()
						lowerCAmelCase__	:						int  				=	PNDMScheduler(skip_prk_steps=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	'''A hammer hitting a wooden surface'''

						# test num_waveforms_per_prompt=1 (default)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe(lowercase_   ,num_inference_steps=2		).audios

						assert audios.shape == (1, 2_5_6)

						# test num_waveforms_per_prompt=1 (default) for batch of prompts
						lowerCAmelCase__	:						List[Any]  				=	2
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe([prompt] * batch_size   ,num_inference_steps=2		).audios

						assert audios.shape == (batch_size, 2_5_6)

						# test num_waveforms_per_prompt for single prompt
						lowerCAmelCase__	:						Dict  				=	2
						lowerCAmelCase__	:						int  				=	audioldm_pipe(lowercase_   ,num_inference_steps=2   ,num_waveforms_per_prompt=lowercase_		).audios

						assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

						# test num_waveforms_per_prompt for batch of prompts
						lowerCAmelCase__	:						Optional[int]  				=	2
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe(
						    [prompt] * batch_size   ,num_inference_steps=2   ,num_waveforms_per_prompt=lowercase_		).audios

						assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Optional[int]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Optional[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)
						lowerCAmelCase__	:						Any  				=	audioldm_pipe.vocoder.config.sampling_rate

						lowerCAmelCase__	:						Any  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe(audio_length_in_s=0.016   ,**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) / vocoder_sampling_rate == 0.016

						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(audio_length_in_s=0.032   ,**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) / vocoder_sampling_rate == 0.032







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						int  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Tuple  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	['''hey''']

						lowerCAmelCase__	:						str  				=	audioldm_pipe(lowercase_   ,num_inference_steps=1		)
						lowerCAmelCase__	:						str  				=	output.audios.shape
						assert audio_shape == (1, 2_5_6)

						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe.vocoder.config
						config.model_in_dim *= 2
						lowerCAmelCase__	:						int  				=	SpeechTaHifiGan(lowercase_		).to(lowercase_		)
						lowerCAmelCase__	:						str  				=	audioldm_pipe(lowercase_   ,num_inference_steps=1		)
						lowerCAmelCase__	:						Dict  				=	output.audios.shape
						# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
						assert audio_shape == (1, 2_5_6)







					def        __lowerCAmelCase (       self			:				Any		):
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict		):
						self._test_inference_batch_single_identical(test_mean_pixel_difference=lowercase_		)







					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_		)







@slow
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				str		):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]="cpu"   ,lowercase_			:				Any=torch.floataa   ,lowercase_			:				Tuple=0		):
						lowerCAmelCase__	:						str  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						str  				=	np.random.RandomState(lowercase_		).standard_normal((1, 8, 1_2_8, 1_6)		)
						lowerCAmelCase__	:						str  				=	torch.from_numpy(lowercase_		).to(device=lowercase_   ,dtype=lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''prompt''': '''A hammer hitting a wooden surface''',
						    '''latents''': latents,
						    '''generator''': generator,
						    '''num_inference_steps''': 3,
						    '''guidance_scale''': 2.5,
						}
						return inputs







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Union[str, Any]  				=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''		)
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	2_5
						lowerCAmelCase__	:						Optional[Any]  				=	audioldm_pipe(**lowercase_		).audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 8_1_9_2_0

						lowerCAmelCase__	:						Optional[Any]  				=	audio[7_7_2_3_0:7_7_2_4_0]
						lowerCAmelCase__	:						Optional[Any]  				=	np.array(
						    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]		)
						lowerCAmelCase__	:						List[str]  				=	np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 1E-2







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Optional[int]  				=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''		)
						lowerCAmelCase__	:						Any  				=	LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config		)
						lowerCAmelCase__	:						str  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	self.get_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(**lowercase_		).audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 8_1_9_2_0

						lowerCAmelCase__	:						List[Any]  				=	audio[2_7_7_8_0:2_7_7_9_0]
						lowerCAmelCase__	:						Any  				=	np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]		)
						lowerCAmelCase__	:						Tuple  				=	np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 3E-2






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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """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		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=None    ,						**snake_case__		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
          snake_case       :   Optional[Any]							=      model
          snake_case       :   Dict							=      kwargs.get("model_save_dir"    ,						snake_case__ )
          snake_case       :   int							=      kwargs.get("latest_model_name"    ,						snake_case__ )



   def __call__(self		:					Tuple    ,						**snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      {k: np.array(snake_case__ ) for k, v in kwargs.items()}
          return self.model.run(snake_case__    ,						snake_case__ )



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


          '''simple docstring'''


          if provider is None:
                 logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
                 snake_case       :   Optional[int]							=      "CPUExecutionProvider"

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



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


          '''simple docstring'''


          snake_case       :   Tuple							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

          snake_case       :   Any							=      self.model_save_dir.joinpath(self.latest_model_name )
          snake_case       :   str							=      Path(snake_case__ ).joinpath(snake_case__ )
          try:
                 shutil.copyfile(snake_case__    ,						snake_case__ )
          except shutil.SameFileError:
                 pass

          # copy external weights (for models >2GB)
          snake_case       :   List[str]							=      self.model_save_dir.joinpath(snake_case__ )
          if src_path.exists():
                 snake_case       :   Tuple							=      Path(snake_case__ ).joinpath(snake_case__ )
                 try:
                        shutil.copyfile(snake_case__    ,						snake_case__ )
                 except shutil.SameFileError:
                        pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, os.PathLike]    ,						**snake_case__		:					Optional[int]    ,						)		->	str:


          '''simple docstring'''


          if os.path.isfile(snake_case__ ):
                 logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
                 return

          os.makedirs(snake_case__    ,						exist_ok=snake_case__ )

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



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Tuple    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[Union[bool, str, None]] = None    ,						snake_case__		:					Optional[Union[str, None]] = None    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional["ort.SessionOptions"] = None    ,						**snake_case__		:					Tuple    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
          # load model from local directory
          if os.path.isdir(snake_case__ ):
                 snake_case       :   Any							=      OnnxRuntimeModel.load_model(
                     os.path.join(snake_case__    ,						snake_case__ )    ,						provider=snake_case__    ,						sess_options=snake_case__ )
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ )
          # load model from hub
          else:
                 # download model
                 snake_case       :   Dict							=      hf_hub_download(
                     repo_id=snake_case__    ,						filename=snake_case__    ,						use_auth_token=snake_case__    ,						revision=snake_case__    ,						cache_dir=snake_case__    ,						force_download=snake_case__    ,						)
                 snake_case       :   List[Any]							=      Path(snake_case__ ).parent
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ ).name
                 snake_case       :   Dict							=      OnnxRuntimeModel.load_model(snake_case__    ,						provider=snake_case__    ,						sess_options=snake_case__ )
          return cls(model=snake_case__    ,						**snake_case__ )



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


          '''simple docstring'''


          snake_case       :   Dict							=      None
          if len(str(snake_case__ ).split("@" ) ) == 2:
                 snake_case				,					snake_case       :   int							=      model_id.split("@" )

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





