






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						self.config_class(**self.inputs_dict   )
							self.parent.assertTrue(hasattr(_UpperCamelCase				,		'hidden_sizes'   )   )
							self.parent.assertTrue(hasattr(_UpperCamelCase				,		'num_attention_heads'   )   )
							self.parent.assertTrue(hasattr(_UpperCamelCase				,		'num_encoder_blocks'   )   )





class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=1_3				,		_UpperCamelCase=6_4				,		_UpperCamelCase=3				,		_UpperCamelCase=4				,		_UpperCamelCase=[2, 2, 2, 2]				,		_UpperCamelCase=[8, 4, 2, 1]				,		_UpperCamelCase=[1_6, 3_2, 6_4, 1_2_8]				,		_UpperCamelCase=[1, 4, 8, 1_6]				,		_UpperCamelCase=[1, 2, 4, 8]				,		_UpperCamelCase=True				,		_UpperCamelCase=True				,		_UpperCamelCase="gelu"				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.02				,		_UpperCamelCase=3				,		_UpperCamelCase=None				,		)	->							List[Any]:
							UpperCAmelCase_    :      Optional[int]            =						parent
							UpperCAmelCase_    :      Union[str, Any]            =						batch_size
							UpperCAmelCase_    :      Dict            =						image_size
							UpperCAmelCase_    :      Dict            =						num_channels
							UpperCAmelCase_    :      Optional[int]            =						num_encoder_blocks
							UpperCAmelCase_    :      Union[str, Any]            =						sr_ratios
							UpperCAmelCase_    :      Optional[Any]            =						depths
							UpperCAmelCase_    :      str            =						hidden_sizes
							UpperCAmelCase_    :      int            =						downsampling_rates
							UpperCAmelCase_    :      Any            =						num_attention_heads
							UpperCAmelCase_    :      Any            =						is_training
							UpperCAmelCase_    :      int            =						use_labels
							UpperCAmelCase_    :      Tuple            =						hidden_act
							UpperCAmelCase_    :      List[str]            =						hidden_dropout_prob
							UpperCAmelCase_    :      Tuple            =						attention_probs_dropout_prob
							UpperCAmelCase_    :      Optional[int]            =						initializer_range
							UpperCAmelCase_    :      Any            =						num_labels
							UpperCAmelCase_    :      List[str]            =						scope



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Dict            =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							UpperCAmelCase_    :      Dict            =						None
							if self.use_labels:
												UpperCAmelCase_    :      Any            =						ids_tensor([self.batch_size, self.image_size, self.image_size]				,		self.num_labels   )

							UpperCAmelCase_    :      str            =						self.get_config()
							return config, pixel_values, labels



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							return SegformerConfig(
							    image_size=self.image_size				,		num_channels=self.num_channels				,		num_encoder_blocks=self.num_encoder_blocks				,		depths=self.depths				,		hidden_sizes=self.hidden_sizes				,		num_attention_heads=self.num_attention_heads				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		initializer_range=self.initializer_range				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      str            =						SegformerModel(config=_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      List[str]            =						model(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape				,		(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[str]:
							UpperCAmelCase_    :      int            =						self.num_labels
							UpperCAmelCase_    :      Optional[Any]            =						SegformerForSemanticSegmentation(_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      Any            =						model(_UpperCamelCase   )
							self.parent.assertEqual(
							    result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
							UpperCAmelCase_    :      int            =						model(_UpperCamelCase				,		labels=_UpperCamelCase   )
							self.parent.assertEqual(
							    result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
							self.parent.assertGreater(result.loss				,		0.0   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Union[str, Any]:
							UpperCAmelCase_    :      List[Any]            =						1
							UpperCAmelCase_    :      Dict            =						SegformerForSemanticSegmentation(config=_UpperCamelCase   )
							model.to(_UpperCamelCase   )
							model.eval()
							UpperCAmelCase_    :      Tuple            =						torch.randint(0				,		1				,		(self.batch_size, self.image_size, self.image_size)   ).to(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						model(_UpperCamelCase				,		labels=_UpperCamelCase   )
							self.parent.assertGreater(result.loss				,		0.0   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      Any            =						self.prepare_config_and_inputs()
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						config_and_inputs
							UpperCAmelCase_    :      Optional[int]            =						{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class   lowerCamelCase      (_snake_case ,  _snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Optional[Any]       						=  (
		    (
		        SegformerModel,
		        SegformerForSemanticSegmentation,
		        SegformerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_snake_case    :    int       						=  (
		    {
		        '''feature-extraction''': SegformerModel,
		        '''image-classification''': SegformerForImageClassification,
		        '''image-segmentation''': SegformerForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_snake_case    :    Optional[Any]       						=  True
		_snake_case    :    Dict       						=  False
		_snake_case    :    Any       						=  False
		_snake_case    :    int       						=  False



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Any            =						SegformerModelTester(self   )
							UpperCAmelCase_    :      int            =						SegformerConfigTester(self				,		config_class=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							self.config_tester.run_common_tests()



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							UpperCAmelCase_    :      str            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							UpperCAmelCase_    :      Dict            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*_UpperCamelCase   )



		@unittest.skip('SegFormer does not use inputs_embeds'   )
		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							pass



		@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'   )
		def 					__UpperCAmelCase			(							self   )	->							str:
							pass



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_    :      List[Any]            =						model_class(_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						inspect.signature(model.forward   )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCAmelCase_    :      int            =						[*signature.parameters.keys()]

												UpperCAmelCase_    :      Union[str, Any]            =						['pixel_values']
												self.assertListEqual(arg_names[:1]				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_    :      Union[str, Any]            =						True

							for model_class in self.all_model_classes:
												UpperCAmelCase_    :      Union[str, Any]            =						True
												UpperCAmelCase_    :      str            =						False
												UpperCAmelCase_    :      List[str]            =						True
												UpperCAmelCase_    :      Optional[Any]            =						model_class(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												with torch.no_grad():
																	UpperCAmelCase_    :      int            =						model(**self._prepare_for_class(_UpperCamelCase				,		_UpperCamelCase   )   )
												UpperCAmelCase_    :      int            =						outputs.attentions

												UpperCAmelCase_    :      Tuple            =						sum(self.model_tester.depths   )
												self.assertEqual(len(_UpperCamelCase   )				,		_UpperCamelCase   )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												UpperCAmelCase_    :      Optional[Any]            =						True
												UpperCAmelCase_    :      Any            =						model_class(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												with torch.no_grad():
																	UpperCAmelCase_    :      int            =						model(**self._prepare_for_class(_UpperCamelCase				,		_UpperCamelCase   )   )
												UpperCAmelCase_    :      Optional[Any]            =						outputs.attentions

												self.assertEqual(len(_UpperCamelCase   )				,		_UpperCamelCase   )

												# verify the first attentions (first block, first layer)
												UpperCAmelCase_    :      Optional[int]            =						(self.model_tester.image_size // 4) ** 2
												UpperCAmelCase_    :      List[Any]            =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
												self.assertListEqual(
												    list(attentions[0].shape[-3:]   )				,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]				,		)

												# verify the last attentions (last block, last layer)
												UpperCAmelCase_    :      int            =						(self.model_tester.image_size // 3_2) ** 2
												UpperCAmelCase_    :      Dict            =						(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
												self.assertListEqual(
												    list(attentions[-1].shape[-3:]   )				,		[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]				,		)
												UpperCAmelCase_    :      List[Any]            =						len(_UpperCamelCase   )

												# Check attention is always last and order is fine
												UpperCAmelCase_    :      Union[str, Any]            =						True
												UpperCAmelCase_    :      Optional[Any]            =						True
												UpperCAmelCase_    :      str            =						model_class(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												with torch.no_grad():
																	UpperCAmelCase_    :      Union[str, Any]            =						model(**self._prepare_for_class(_UpperCamelCase				,		_UpperCamelCase   )   )

												self.assertEqual(out_len + 1				,		len(_UpperCamelCase   )   )

												UpperCAmelCase_    :      List[str]            =						outputs.attentions

												self.assertEqual(len(_UpperCamelCase   )				,		_UpperCamelCase   )
												# verify the first attentions (first block, first layer)
												UpperCAmelCase_    :      Union[str, Any]            =						(self.model_tester.image_size // 4) ** 2
												UpperCAmelCase_    :      List[Any]            =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]   )				,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]				,		)



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							def check_hidden_states_output(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Dict            =						model_class(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()

												with torch.no_grad():
																	UpperCAmelCase_    :      List[str]            =						model(**self._prepare_for_class(_UpperCamelCase				,		_UpperCamelCase   )   )

												UpperCAmelCase_    :      Dict            =						outputs.hidden_states

												UpperCAmelCase_    :      Optional[int]            =						self.model_tester.num_encoder_blocks
												self.assertEqual(len(_UpperCamelCase   )				,		_UpperCamelCase   )

												# verify the first hidden states (first block)
												self.assertListEqual(
												    list(hidden_states[0].shape[-3:]   )				,		[
												        self.model_tester.hidden_sizes[0],
												        self.model_tester.image_size // 4,
												        self.model_tester.image_size // 4,
												    ]				,		)

							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_    :      List[Any]            =						True
												check_hidden_states_output(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCAmelCase_    :      List[Any]            =						True

												check_hidden_states_output(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							if not self.model_tester.is_training:
												return

							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_    :      str            =						True

							for model_class in self.all_model_classes:
												if model_class in get_values(_UpperCamelCase   ):
																	continue

												UpperCAmelCase_    :      str            =						model_class(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.train()
												UpperCAmelCase_    :      int            =						self._prepare_for_class(_UpperCamelCase				,		_UpperCamelCase				,		return_labels=_UpperCamelCase   )
												UpperCAmelCase_    :      str            =						model(**_UpperCamelCase   ).loss
												loss.backward()



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							pass



		@slow
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_    :      List[Any]            =						SegformerModel.from_pretrained(_UpperCamelCase   )
												self.assertIsNotNone(_UpperCamelCase   )





def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image






@require_torch
class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		@slow
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							# only resize + normalize
							UpperCAmelCase_    :      int            =						SegformerImageProcessor(
							    image_scale=(5_1_2, 5_1_2)				,		keep_ratio=_UpperCamelCase				,		align=_UpperCamelCase				,		do_random_crop=_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'   ).to(
							    _UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						prepare_img()
							UpperCAmelCase_    :      str            =						image_processor(images=_UpperCamelCase				,		return_tensors='pt'   )
							UpperCAmelCase_    :      Tuple            =						encoded_inputs.pixel_values.to(_UpperCamelCase   )

							with torch.no_grad():
												UpperCAmelCase_    :      int            =						model(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)   )
							self.assertEqual(outputs.logits.shape				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						torch.tensor(
							    [
							        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
							        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
							        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
							    ]   ).to(_UpperCamelCase   )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]				,		_UpperCamelCase				,		atol=1E-4   )   )



		@slow
		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							# only resize + normalize
							UpperCAmelCase_    :      Optional[Any]            =						SegformerImageProcessor(
							    image_scale=(5_1_2, 5_1_2)				,		keep_ratio=_UpperCamelCase				,		align=_UpperCamelCase				,		do_random_crop=_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						SegformerForSemanticSegmentation.from_pretrained(
							    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'   ).to(_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						prepare_img()
							UpperCAmelCase_    :      Union[str, Any]            =						image_processor(images=_UpperCamelCase				,		return_tensors='pt'   )
							UpperCAmelCase_    :      Union[str, Any]            =						encoded_inputs.pixel_values.to(_UpperCamelCase   )

							with torch.no_grad():
												UpperCAmelCase_    :      List[str]            =						model(_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)   )
							self.assertEqual(outputs.logits.shape				,		_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						torch.tensor(
							    [
							        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
							        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
							        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
							    ]   ).to(_UpperCamelCase   )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]				,		_UpperCamelCase				,		atol=1E-1   )   )



		@slow
		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							# only resize + normalize
							UpperCAmelCase_    :      int            =						SegformerImageProcessor(
							    image_scale=(5_1_2, 5_1_2)				,		keep_ratio=_UpperCamelCase				,		align=_UpperCamelCase				,		do_random_crop=_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'   ).to(
							    _UpperCamelCase   )

							UpperCAmelCase_    :      int            =						prepare_img()
							UpperCAmelCase_    :      str            =						image_processor(images=_UpperCamelCase				,		return_tensors='pt'   )
							UpperCAmelCase_    :      Any            =						encoded_inputs.pixel_values.to(_UpperCamelCase   )

							with torch.no_grad():
												UpperCAmelCase_    :      List[Any]            =						model(_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						outputs.logits.detach().cpu()

							UpperCAmelCase_    :      Tuple            =						image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase				,		target_sizes=[(5_0_0, 3_0_0)]   )
							UpperCAmelCase_    :      Tuple            =						torch.Size((5_0_0, 3_0_0)   )
							self.assertEqual(segmentation[0].shape				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						torch.Size((1_2_8, 1_2_8)   )
							self.assertEqual(segmentation[0].shape				,		_UpperCamelCase   )














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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    '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   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							Dict:
							logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'   )
							UpperCAmelCase_    :      Any            =						model
							UpperCAmelCase_    :      int            =						kwargs.get('model_save_dir'				,		_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						kwargs.get('latest_model_name'				,		_UpperCamelCase   )



		def __call__(							self				,		**_UpperCamelCase   )	->							str:
							UpperCAmelCase_    :      Optional[int]            =						{k: np.array(_UpperCamelCase   ) for k, v in kwargs.items()}
							return self.model.run(_UpperCamelCase				,		_UpperCamelCase   )



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase=None				,		_UpperCamelCase=None   )	->							List[Any]:
							if provider is None:
												logger.info('No onnxruntime provider specified, using CPUExecutionProvider'   )
												UpperCAmelCase_    :      List[str]            =						'CPUExecutionProvider'

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Any            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

							UpperCAmelCase_    :      Optional[Any]            =						self.model_save_dir.joinpath(self.latest_model_name   )
							UpperCAmelCase_    :      str            =						Path(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
							try:
												shutil.copyfile(_UpperCamelCase				,		_UpperCamelCase   )
							except shutil.SameFileError:
												pass

							# copy external weights (for models >2GB)
							UpperCAmelCase_    :      Optional[Any]            =						self.model_save_dir.joinpath(_UpperCamelCase   )
							if src_path.exists():
												UpperCAmelCase_    :      List[Any]            =						Path(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
												try:
																	shutil.copyfile(_UpperCamelCase				,		_UpperCamelCase   )
												except shutil.SameFileError:
																	pass



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

							os.makedirs(_UpperCamelCase				,		exist_ok=_UpperCamelCase   )

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



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



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						None
							if len(str(_UpperCamelCase   ).split('@'   )   ) == 2:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						model_id.split('@'   )

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






