




import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin




class 			UpperCAmelCase_      (   __lowerCamelCase       ):




							'''simple docstring'''



							a__						  =			None
							a__						  =			None





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					List[Any]:
										return self.feat_extract_tester.prepare_feat_extract_dict()





							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										self.assertTrue(hasattr(UpperCamelCase_       ,     """feature_size"""			)			)
										self.assertTrue(hasattr(UpperCamelCase_       ,     """sampling_rate"""			)			)
										self.assertTrue(hasattr(UpperCamelCase_       ,     """padding_value"""			)			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common()
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}			)

										self.assertTrue(all(len(UpperCamelCase_			) == len(UpperCamelCase_			) for x, y in zip(UpperCamelCase_       ,     processed_features[input_name]			)			)			)

										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}       ,     tensor_type="""np"""			)

										SCREAMING_SNAKE_CASE           =					processed_features[input_name]

										if len(batch_features_input.shape			) < 3:
													SCREAMING_SNAKE_CASE           =					batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)





							@require_torch
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					str:
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}       ,     tensor_type="""pt"""			)

										SCREAMING_SNAKE_CASE           =					processed_features[input_name]

										if len(batch_features_input.shape			) < 3:
													SCREAMING_SNAKE_CASE           =					batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)





							@require_tf
							def    _UpperCAmelCase     (					self       :	int			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}       ,     tensor_type="""tf"""			)

										SCREAMING_SNAKE_CASE           =					processed_features[input_name]

										if len(batch_features_input.shape			) < 3:
													SCREAMING_SNAKE_CASE           =					batch_features_input[:, :, None]

										self.assertTrue(
										    batch_features_input.shape
										    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]			), self.feat_extract_tester.feature_size)			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Dict=False			)			->					List[Any]:
										def _inputs_have_equal_length(a       :	Tuple			):
													SCREAMING_SNAKE_CASE           =					len(input[0]			)
													for input_slice in input[1:]:
																if len(UpperCamelCase_			) != length:
																			return False
													return True

										def _inputs_are_equal(a       :	str       ,     a       :	Optional[int]			):
													if len(UpperCamelCase_			) != len(UpperCamelCase_			):
																return False

													for input_slice_a, input_slice_a in zip(UpperCamelCase_       ,     UpperCamelCase_			):
																if not np.allclose(np.asarray(UpperCamelCase_			)       ,     np.asarray(UpperCamelCase_			)       ,     atol=1E-3			):
																			return False
													return True

										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}			)

										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.seq_length_diff
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.max_seq_length + pad_diff
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.min_seq_length
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.batch_size
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.feature_size

										# test padding for List[int] + numpy
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[-1]			)			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     return_tensors="""np"""			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										# max_length parameter has to be provided when setting `padding="max_length"`
										with self.assertRaises(UpperCamelCase_			):
													feat_extract.pad(UpperCamelCase_       ,     padding="""max_length"""			)[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=UpperCamelCase_       ,     return_tensors="""np"""			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertTrue(_inputs_are_equal(UpperCamelCase_       ,     UpperCamelCase_			)			)
										self.assertTrue(len(input_a[0]			) == pad_min_length			)
										self.assertTrue(len(input_a[1]			) == pad_min_length + pad_diff			)
										self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]			))			)
										self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)			)

										if feature_size > 1:
													self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size			)

										# test padding for `pad_to_multiple_of` for List[int] + numpy
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     pad_to_multiple_of=10			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     pad_to_multiple_of=10			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     pad_to_multiple_of=10       ,     max_length=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     pad_to_multiple_of=10       ,     max_length=UpperCamelCase_       ,     return_tensors="""np"""       ,     )
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										self.assertTrue(all(len(UpperCamelCase_			) % 10 == 0 for x in input_a			)			)
										self.assertTrue(_inputs_are_equal(UpperCamelCase_       ,     UpperCamelCase_			)			)

										SCREAMING_SNAKE_CASE           =					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
										self.assertTrue(all(len(UpperCamelCase_			) == expected_mult_pad_length for x in input_a			)			)
										self.assertEqual(input_a.shape[:2]       ,     (batch_size, expected_mult_pad_length)			)

										if feature_size > 1:
													self.assertTrue(input_a.shape[2] == feature_size			)

										# Check padding value is correct
										SCREAMING_SNAKE_CASE           =					(np.ones(self.feat_extract_tester.feature_size			) * feat_extract.padding_value).sum()
										self.assertTrue(
										    abs(np.asarray(input_a[0]			)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			)
										    < 1E-3			)
										self.assertTrue(
										    abs(
										        np.asarray(input_a[1]			)[pad_min_length + pad_diff :].sum()
										        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)			)
										    < 1E-3			)
										self.assertTrue(
										    abs(
										        np.asarray(input_a[2]			)[pad_min_length + 2 * pad_diff :].sum()
										        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)			)
										    < 1E-3			)
										self.assertTrue(
										    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			) < 1E-3			)
										self.assertTrue(
										    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)			)
										    < 1E-3			)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	Any=False			)			->					Union[str, Any]:
										def _inputs_have_equal_length(a       :	List[str]			):
													SCREAMING_SNAKE_CASE           =					len(input[0]			)
													for input_slice in input[1:]:
																if len(UpperCamelCase_			) != length:
																			return False
													return True

										def _inputs_are_equal(a       :	List[Any]       ,     a       :	int			):
													if len(UpperCamelCase_			) != len(UpperCamelCase_			):
																return False

													for input_slice_a, input_slice_a in zip(UpperCamelCase_       ,     UpperCamelCase_			):
																if not np.allclose(np.asarray(UpperCamelCase_			)       ,     np.asarray(UpperCamelCase_			)       ,     atol=1E-3			):
																			return False
													return True

										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}			)

										# truncate to smallest
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[0]			)       ,     truncation=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[0]			)			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)

										# truncate to smallest with np
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[0]			)       ,     return_tensors="""np"""       ,     truncation=UpperCamelCase_       ,     )
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[0]			)       ,     return_tensors="""np"""			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertTrue(input_a.shape[1] == len(speech_inputs[0]			)			)

										# since truncation forces padding to be smaller than longest input
										# function can't return `np.ndarray`, but has to return list
										self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)

										# truncate to middle
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[1]			)       ,     truncation=UpperCamelCase_       ,     return_tensors="""np"""       ,     )
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[1]			)       ,     truncation=UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[1]			)       ,     return_tensors="""np"""			)
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										self.assertTrue(input_a.shape[1] == len(speech_inputs[1]			)			)
										self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertTrue(_inputs_are_equal(UpperCamelCase_       ,     UpperCamelCase_			)			)

										# since truncation forces padding to be smaller than longest input
										# function can't return `np.ndarray`, but has to return list
										self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertTrue(len(input_a[-1]			) == len(speech_inputs[-1]			)			)

										# padding has to be max_length when setting `truncation=True`
										with self.assertRaises(UpperCamelCase_			):
													feat_extract.pad(UpperCamelCase_       ,     truncation=UpperCamelCase_			)[input_name]

										# padding has to be max_length when setting `truncation=True`
										with self.assertRaises(UpperCamelCase_			):
													feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     truncation=UpperCamelCase_			)[input_name]

										# padding has to be max_length when setting `truncation=True`
										with self.assertRaises(UpperCamelCase_			):
													feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     truncation=UpperCamelCase_			)[input_name]

										# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
										with self.assertRaises(UpperCamelCase_			):
													feat_extract.pad(UpperCamelCase_       ,     padding="""max_length"""       ,     truncation=UpperCamelCase_			)[input_name]

										# test truncation for `pad_to_multiple_of` for List[int] + numpy
										SCREAMING_SNAKE_CASE           =					12
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[0]			)       ,     pad_to_multiple_of=UpperCamelCase_       ,     truncation=UpperCamelCase_       ,     )
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=len(speech_inputs[0]			)       ,     pad_to_multiple_of=UpperCamelCase_       ,     )
										SCREAMING_SNAKE_CASE           =					input_a[input_name]

										# retrieve expected_length as multiple of pad_to_multiple_of
										SCREAMING_SNAKE_CASE           =					len(speech_inputs[0]			)
										if expected_length % pad_to_multiple_of != 0:
													SCREAMING_SNAKE_CASE           =					((len(speech_inputs[0]			) // pad_to_multiple_of) + 1) * pad_to_multiple_of

										self.assertTrue(len(input_a[0]			) == expected_length			)
										self.assertTrue(_inputs_have_equal_length(UpperCamelCase_			)			)
										self.assertFalse(_inputs_have_equal_length(UpperCamelCase_			)			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Any:
										self._check_padding(numpify=UpperCamelCase_			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					List[Any]:
										self._check_padding(numpify=UpperCamelCase_			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Any:
										self._check_truncation(numpify=UpperCamelCase_			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										self._check_truncation(numpify=UpperCamelCase_			)





							@require_torch
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common()
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}			)

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     return_tensors="""np"""			)[input_name]
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     return_tensors="""pt"""			)[input_name]

										self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_pt.numpy().astype(np.floataa			).sum()			) < 1E-2			)





							@require_tf
							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**self.feat_extract_dict			)
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common()
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}			)

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     return_tensors="""np"""			)[input_name]
										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     return_tensors="""tf"""			)[input_name]

										self.assertTrue(abs(input_np.astype(np.floataa			).sum() - input_tf.numpy().astype(np.floataa			).sum()			) < 1E-2			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self.feat_extract_dict
										SCREAMING_SNAKE_CASE           =					True
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common()
										SCREAMING_SNAKE_CASE           =					[len(UpperCamelCase_			) for x in speech_inputs]
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}			)

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(UpperCamelCase_       ,     padding="""longest"""       ,     return_tensors="""np"""			)
										self.assertIn("""attention_mask"""       ,     UpperCamelCase_			)
										self.assertListEqual(list(processed.attention_mask.shape			)       ,     list(processed[input_name].shape[:2]			)			)
										self.assertListEqual(processed.attention_mask.sum(-1			).tolist()       ,     UpperCamelCase_			)







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.feat_extract_dict
										SCREAMING_SNAKE_CASE           =					True
										SCREAMING_SNAKE_CASE           =					self.feature_extraction_class(**UpperCamelCase_			)
										SCREAMING_SNAKE_CASE           =					self.feat_extract_tester.prepare_inputs_for_common()
										SCREAMING_SNAKE_CASE           =					[len(UpperCamelCase_			) for x in speech_inputs]
										SCREAMING_SNAKE_CASE           =					feat_extract.model_input_names[0]

										SCREAMING_SNAKE_CASE           =					BatchFeature({input_name: speech_inputs}			)
										SCREAMING_SNAKE_CASE           =					min(UpperCamelCase_			)

										SCREAMING_SNAKE_CASE           =					feat_extract.pad(
										    UpperCamelCase_       ,     padding="""max_length"""       ,     max_length=UpperCamelCase_       ,     truncation=UpperCamelCase_       ,     return_tensors="""np"""			)
										self.assertIn("""attention_mask"""       ,     UpperCamelCase_			)
										self.assertListEqual(
										    list(processed_pad.attention_mask.shape			)       ,     [processed_pad[input_name].shape[0], max_length]			)
										self.assertListEqual(
										    processed_pad.attention_mask[:, :max_length].sum(-1			).tolist()       ,     [max_length for x in speech_inputs]			)












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


__A						:							str							=  logging.get_logger(__name__)

__A						:							str							=  {
    """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]       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					List[str]:
										logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""			)
										SCREAMING_SNAKE_CASE           =					model
										SCREAMING_SNAKE_CASE           =					kwargs.get("""model_save_dir"""       ,     a			)
										SCREAMING_SNAKE_CASE           =					kwargs.get("""latest_model_name"""       ,     a			)





							def __call__(					self       :	List[str]       ,     **a       :	Any			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					{k: np.array(a			) for k, v in kwargs.items()}
										return self.model.run(a       ,     a			)





							@staticmethod
							def    _UpperCAmelCase     (					a       :	Union[str, Path]       ,     a       :	Any=None       ,     a       :	Optional[int]=None			)			->					Optional[Any]:
										if provider is None:
													logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""			)
													SCREAMING_SNAKE_CASE           =					"""CPUExecutionProvider"""

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





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	Union[str, Path]       ,     a       :	Optional[str] = None       ,     **a       :	str			)			->					Tuple:
										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(a			).joinpath(a			)
										try:
													shutil.copyfile(a       ,     a			)
										except shutil.SameFileError:
													pass

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





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Union[str, os.PathLike]       ,     **a       :	Tuple       ,     )			->					str:
										if os.path.isfile(a			):
													logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""			)
													return

										os.makedirs(a       ,     exist_ok=a			)

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





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







							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]       ,     a       :	Union[str, Path]       ,     a       :	bool = True       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     **a       :	Union[str, Any]       ,     )			->					Any:
										SCREAMING_SNAKE_CASE           =					None
										if len(str(a			).split("""@"""			)			) == 2:
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					model_id.split("""@"""			)

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






