





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_    =				logging.get_logger(__name__)






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				snake_case_						:    Dict				=  DPTConfig(embedding_type='''hybrid''' )

				if "large" in checkpoint_url:
								snake_case_						:    str				=  1_024
								snake_case_						:    List[str]				=  4_096
								snake_case_						:    List[str]				=  24
								snake_case_						:    Union[str, Any]				=  16
								snake_case_						:    Union[str, Any]				=  [5, 11, 17, 23]
								snake_case_						:    List[str]				=  [256, 512, 1_024, 1_024]
								snake_case_						:    List[Any]				=  (1, 384, 384)

				if "nyu" or "midas" in checkpoint_url:
								snake_case_						:    str				=  768
								snake_case_						:    Optional[Any]				=  [1, 1, 1, 0.5]
								snake_case_						:    Optional[int]				=  [256, 512, 768, 768]
								snake_case_						:    List[Any]				=  150
								snake_case_						:    Dict				=  16
								snake_case_						:    List[str]				=  (1, 384, 384)
								snake_case_						:    Tuple				=  False
								snake_case_						:    Any				=  '''project'''

				if "ade" in checkpoint_url:
								snake_case_						:    Any				=  True
								snake_case_						:    Optional[Any]				=  768
								snake_case_						:    List[str]				=  [1, 1, 1, 0.5]
								snake_case_						:    Dict				=  150
								snake_case_						:    Optional[int]				=  16
								snake_case_						:    int				=  '''huggingface/label-files'''
								snake_case_						:    Dict				=  '''ade20k-id2label.json'''
								snake_case_						:    Any				=  json.load(open(cached_download(hf_hub_url(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset''' ) ) ,				'''r''' ) )
								snake_case_						:    List[str]				=  {int(_UpperCamelCase ): v for k, v in idalabel.items()}
								snake_case_						:    List[str]				=  idalabel
								snake_case_						:    List[Any]				=  {v: k for k, v in idalabel.items()}
								snake_case_						:    int				=  [1, 150, 480, 480]

				return config, expected_shape






def      lowerCamelCase_						(				_UpperCamelCase )		->						Dict:






				"""simple docstring"""


				snake_case_						:    Tuple				=  ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(_UpperCamelCase ,				_UpperCamelCase )






def      lowerCamelCase_						(				_UpperCamelCase )		->						str:






				"""simple docstring"""


				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								snake_case_						:    Union[str, Any]				=  name.replace('''pretrained.model''' ,				'''dpt.encoder''' )
				if "pretrained.model" in name:
								snake_case_						:    Any				=  name.replace('''pretrained.model''' ,				'''dpt.embeddings''' )
				if "patch_embed" in name:
								snake_case_						:    str				=  name.replace('''patch_embed''' ,				'''''' )
				if "pos_embed" in name:
								snake_case_						:    Dict				=  name.replace('''pos_embed''' ,				'''position_embeddings''' )
				if "attn.proj" in name:
								snake_case_						:    Dict				=  name.replace('''attn.proj''' ,				'''attention.output.dense''' )
				if "proj" in name and "project" not in name:
								snake_case_						:    Tuple				=  name.replace('''proj''' ,				'''projection''' )
				if "blocks" in name:
								snake_case_						:    Any				=  name.replace('''blocks''' ,				'''layer''' )
				if "mlp.fc1" in name:
								snake_case_						:    Any				=  name.replace('''mlp.fc1''' ,				'''intermediate.dense''' )
				if "mlp.fc2" in name:
								snake_case_						:    Dict				=  name.replace('''mlp.fc2''' ,				'''output.dense''' )
				if "norm1" in name and "backbone" not in name:
								snake_case_						:    List[Any]				=  name.replace('''norm1''' ,				'''layernorm_before''' )
				if "norm2" in name and "backbone" not in name:
								snake_case_						:    Optional[int]				=  name.replace('''norm2''' ,				'''layernorm_after''' )
				if "scratch.output_conv" in name:
								snake_case_						:    str				=  name.replace('''scratch.output_conv''' ,				'''head''' )
				if "scratch" in name:
								snake_case_						:    List[Any]				=  name.replace('''scratch''' ,				'''neck''' )
				if "layer1_rn" in name:
								snake_case_						:    List[Any]				=  name.replace('''layer1_rn''' ,				'''convs.0''' )
				if "layer2_rn" in name:
								snake_case_						:    List[str]				=  name.replace('''layer2_rn''' ,				'''convs.1''' )
				if "layer3_rn" in name:
								snake_case_						:    Dict				=  name.replace('''layer3_rn''' ,				'''convs.2''' )
				if "layer4_rn" in name:
								snake_case_						:    List[Any]				=  name.replace('''layer4_rn''' ,				'''convs.3''' )
				if "refinenet" in name:
								snake_case_						:    Dict				=  int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								snake_case_						:    str				=  name.replace(f'''refinenet{layer_idx}''' ,				f'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
				if "out_conv" in name:
								snake_case_						:    List[str]				=  name.replace('''out_conv''' ,				'''projection''' )
				if "resConfUnit1" in name:
								snake_case_						:    int				=  name.replace('''resConfUnit1''' ,				'''residual_layer1''' )
				if "resConfUnit2" in name:
								snake_case_						:    Optional[int]				=  name.replace('''resConfUnit2''' ,				'''residual_layer2''' )
				if "conv1" in name:
								snake_case_						:    Any				=  name.replace('''conv1''' ,				'''convolution1''' )
				if "conv2" in name:
								snake_case_						:    Optional[Any]				=  name.replace('''conv2''' ,				'''convolution2''' )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								snake_case_						:    Union[str, Any]				=  name.replace('''pretrained.act_postprocess1.0.project.0''' ,				'''neck.reassemble_stage.readout_projects.0.0''' )
				if "pretrained.act_postprocess2.0.project.0" in name:
								snake_case_						:    Union[str, Any]				=  name.replace('''pretrained.act_postprocess2.0.project.0''' ,				'''neck.reassemble_stage.readout_projects.1.0''' )
				if "pretrained.act_postprocess3.0.project.0" in name:
								snake_case_						:    Any				=  name.replace('''pretrained.act_postprocess3.0.project.0''' ,				'''neck.reassemble_stage.readout_projects.2.0''' )
				if "pretrained.act_postprocess4.0.project.0" in name:
								snake_case_						:    str				=  name.replace('''pretrained.act_postprocess4.0.project.0''' ,				'''neck.reassemble_stage.readout_projects.3.0''' )

				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								snake_case_						:    Dict				=  name.replace('''pretrained.act_postprocess1.3''' ,				'''neck.reassemble_stage.layers.0.projection''' )
				if "pretrained.act_postprocess1.4" in name:
								snake_case_						:    Dict				=  name.replace('''pretrained.act_postprocess1.4''' ,				'''neck.reassemble_stage.layers.0.resize''' )
				if "pretrained.act_postprocess2.3" in name:
								snake_case_						:    Optional[Any]				=  name.replace('''pretrained.act_postprocess2.3''' ,				'''neck.reassemble_stage.layers.1.projection''' )
				if "pretrained.act_postprocess2.4" in name:
								snake_case_						:    Optional[Any]				=  name.replace('''pretrained.act_postprocess2.4''' ,				'''neck.reassemble_stage.layers.1.resize''' )
				if "pretrained.act_postprocess3.3" in name:
								snake_case_						:    Optional[int]				=  name.replace('''pretrained.act_postprocess3.3''' ,				'''neck.reassemble_stage.layers.2.projection''' )
				if "pretrained.act_postprocess4.3" in name:
								snake_case_						:    Any				=  name.replace('''pretrained.act_postprocess4.3''' ,				'''neck.reassemble_stage.layers.3.projection''' )
				if "pretrained.act_postprocess4.4" in name:
								snake_case_						:    str				=  name.replace('''pretrained.act_postprocess4.4''' ,				'''neck.reassemble_stage.layers.3.resize''' )
				if "pretrained" in name:
								snake_case_						:    List[Any]				=  name.replace('''pretrained''' ,				'''dpt''' )
				if "bn" in name:
								snake_case_						:    int				=  name.replace('''bn''' ,				'''batch_norm''' )
				if "head" in name:
								snake_case_						:    str				=  name.replace('''head''' ,				'''head.head''' )
				if "encoder.norm" in name:
								snake_case_						:    List[Any]				=  name.replace('''encoder.norm''' ,				'''layernorm''' )
				if "auxlayer" in name:
								snake_case_						:    Any				=  name.replace('''auxlayer''' ,				'''auxiliary_head.head''' )
				if "backbone" in name:
								snake_case_						:    List[str]				=  name.replace('''backbone''' ,				'''backbone.bit.encoder''' )

				if ".." in name:
								snake_case_						:    int				=  name.replace('''..''' ,				'''.''' )

				if "stem.conv" in name:
								snake_case_						:    int				=  name.replace('''stem.conv''' ,				'''bit.embedder.convolution''' )
				if "blocks" in name:
								snake_case_						:    List[str]				=  name.replace('''blocks''' ,				'''layers''' )
				if "convolution" in name and "backbone" in name:
								snake_case_						:    str				=  name.replace('''convolution''' ,				'''conv''' )
				if "layer" in name and "backbone" in name:
								snake_case_						:    List[Any]				=  name.replace('''layer''' ,				'''layers''' )
				if "backbone.bit.encoder.bit" in name:
								snake_case_						:    Union[str, Any]				=  name.replace('''backbone.bit.encoder.bit''' ,				'''backbone.bit''' )
				if "embedder.conv" in name:
								snake_case_						:    Optional[Any]				=  name.replace('''embedder.conv''' ,				'''embedder.convolution''' )
				if "backbone.bit.encoder.stem.norm" in name:
								snake_case_						:    List[Any]				=  name.replace('''backbone.bit.encoder.stem.norm''' ,				'''backbone.bit.embedder.norm''' )
				return name






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				for i in range(config.num_hidden_layers ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								snake_case_						:    Dict				=  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
								snake_case_						:    Any				=  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
								# next, add query, keys and values (in that order) to the state dict
								snake_case_						:    List[Any]				=  in_proj_weight[: config.hidden_size, :]
								snake_case_						:    int				=  in_proj_bias[: config.hidden_size]
								snake_case_						:    Dict				=  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								snake_case_						:    str				=  in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								snake_case_						:    Tuple				=  in_proj_weight[
								    -config.hidden_size :, :
								]
								snake_case_						:    Dict				=  in_proj_bias[-config.hidden_size :]






def      lowerCamelCase_						(				)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    int				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:    Dict				=  Image.open(requests.get(_UpperCamelCase ,				stream=_UpperCamelCase ).raw )
				return im






@torch.no_grad()
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


				snake_case_			,							snake_case_						:    Dict				=  get_dpt_config(_UpperCamelCase )
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				snake_case_						:    Union[str, Any]				=  torch.load(_UpperCamelCase ,				map_location='''cpu''' )
				# remove certain keys
				remove_ignore_keys_(_UpperCamelCase )
				# rename keys
				for key in state_dict.copy().keys():
								snake_case_						:    Dict				=  state_dict.pop(_UpperCamelCase )
								snake_case_						:    Any				=  val
				# read in qkv matrices
				read_in_q_k_v(_UpperCamelCase ,				_UpperCamelCase )

				# load HuggingFace model
				snake_case_						:    str				=  DPTForSemanticSegmentation(_UpperCamelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_UpperCamelCase )
				model.load_state_dict(_UpperCamelCase )
				model.eval()

				# Check outputs on an image
				snake_case_						:    Tuple				=  480 if '''ade''' in checkpoint_url else 384
				snake_case_						:    List[Any]				=  DPTImageProcessor(size=_UpperCamelCase )

				snake_case_						:    Any				=  prepare_img()
				snake_case_						:    Optional[int]				=  image_processor(_UpperCamelCase ,				return_tensors='''pt''' )

				# forward pass
				snake_case_						:    Optional[int]				=  model(**_UpperCamelCase ).logits if '''ade''' in checkpoint_url else model(**_UpperCamelCase ).predicted_depth

				if show_prediction:
								snake_case_						:    Dict				=  (
								    torch.nn.functional.interpolate(
								        outputs.unsqueeze(1 ) ,				size=(image.size[1], image.size[0]) ,				mode='''bicubic''' ,				align_corners=_UpperCamelCase ,				)
								    .squeeze()
								    .cpu()
								    .numpy()
								)

								Image.fromarray((prediction / prediction.max()) * 255 ).show()

				if pytorch_dump_folder_path is not None:
								Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
								print(f'''Saving model to {pytorch_dump_folder_path}''' )
								model.save_pretrained(_UpperCamelCase )
								print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
								image_processor.save_pretrained(_UpperCamelCase )

				if push_to_hub:
								model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
								image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
	    type=str,
	    help='''URL of the original DPT checkpoint you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default=None,
	    type=str,
	    required=False,
	    help='''Path to the output PyTorch model directory.''',
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	)
	parser.add_argument(
	    '''--model_name''',
	    default='''dpt-large''',
	    type=str,
	    help='''Name of the model, in case you\'re pushing to the hub.''',
	)
	parser.add_argument(
	    '''--show_prediction''',
	    action='''store_true''',
	)

	lowerCAmelCase_    =				parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)







import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       (			unittest.TestCase     ):
			lowerCamelCase_     :     Dict       			=      [
			    # (model_name, model_kwargs)
			    ('''bert-base-cased''', {}),
			    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
			]






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    str				=  ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
							with NamedTemporaryFile(mode='''w+t'''    ) as vocab_file:
											vocab_file.write('''\n'''.join(__magic_name__    )    )
											vocab_file.flush()
											snake_case_						:    Optional[Any]				=  BertTokenizerFast(vocab_file.name    )

							with TemporaryDirectory() as bert_save_dir:
											snake_case_						:    str				=  BertModel(BertConfig(vocab_size=len(__magic_name__    )    )    )
											model.save_pretrained(__magic_name__    )
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							__magic_name__    )






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Tuple				=  self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )
											snake_case_						:    List[str]				=  quantize(Path(__magic_name__    )    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Any				=  self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )
											snake_case_						:    Any				=  quantize(__magic_name__    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=None					,							**__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							try:
											# Compute path
											with TemporaryDirectory() as tempdir:
															snake_case_						:    List[str]				=  Path(__magic_name__    ).joinpath('''model.onnx'''    )

											# Remove folder if exists
											if path.parent.exists():
															path.parent.rmdir()

											# Export
											convert(__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )

											return path
							except Exception as e:
											self.fail(__magic_name__    )






			@require_torch
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    Optional[Any]				=  BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    int				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''pt'''    )






			@require_tf
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							from transformers import TFBertModel

							snake_case_						:    Any				=  TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    str				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''tf'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Tuple				=  FeatureExtractionPipeline(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    Optional[int]				=  infer_shapes(__magic_name__					,							__magic_name__    )

							# Assert all variables are present
							self.assertEqual(len(__magic_name__    )					,							len(__magic_name__    )    )
							self.assertTrue(all(var_name in shapes for var_name in variable_names    )    )
							self.assertSequenceEqual(variable_names[:3]					,							__magic_name__    )
							self.assertSequenceEqual(variable_names[3:]					,							__magic_name__    )

							# Assert inputs are {0: batch, 1: sequence}
							for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
											self.assertDictEqual(shapes[var_name]					,							{0: '''batch''', 1: '''sequence'''}    )

							# Assert outputs are {0: batch, 1: sequence} and {0: batch}
							self.assertDictEqual(shapes['''output_0''']					,							{0: '''batch''', 1: '''sequence'''}    )
							self.assertDictEqual(shapes['''output_1''']					,							{0: '''batch'''}    )






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  ['''input_ids''', '''attention_mask''', '''token_type_ids''']
							snake_case_						:    List[str]				=  {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
							snake_case_			,							snake_case_						:    Tuple				=  ensure_valid_input(FuncContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the same number of args (all are valid)
							self.assertEqual(len(__magic_name__    )					,							3    )

							# Should have exactly the same input names
							self.assertEqual(set(__magic_name__    )					,							set(__magic_name__    )    )

							# Parameter should be reordered according to their respective place in the function:
							# (input_ids, token_type_ids, attention_mask)
							self.assertEqual(__magic_name__					,							(tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask'''])    )

							# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
							snake_case_			,							snake_case_						:    Dict				=  ensure_valid_input(FuncNonContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the one arg (all before the one not provided "some_other_args")
							self.assertEqual(len(__magic_name__    )					,							1    )
							self.assertEqual(len(__magic_name__    )					,							1    )

							# Should have only "input_ids"
							self.assertEqual(inputs_args[0]					,							tokens['''input_ids''']    )
							self.assertEqual(ordered_input_names[0]					,							'''input_ids'''    )






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''    )					,							'''-test'''    )
							self.assertEqual('''/home/something/my_fake_model-test.onnx'''					,							generated.as_posix()    )
