


"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)




def   snake_case		(     UpperCamelCase__       :						Optional[Any]    )   ->      int:
      lowerCamelCase					:					str        =	MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            lowerCamelCase					:					Tuple        =	[144, 192, 240]
            lowerCamelCase					:					List[str]        =	[16, 32, 64, 96, 128, 160, 640]
      elif "mobilevit_xs" in mobilevit_name:
            lowerCamelCase					:					Tuple        =	[96, 120, 144]
            lowerCamelCase					:					int        =	[16, 32, 48, 64, 80, 96, 384]
      elif "mobilevit_xxs" in mobilevit_name:
            lowerCamelCase					:					Dict        =	[64, 80, 96]
            lowerCamelCase					:					List[str]        =	[16, 16, 24, 48, 64, 80, 320]
            lowerCamelCase					:					str        =	0.0_5
            lowerCamelCase					:					Dict        =	2.0

      if mobilevit_name.startswith("""deeplabv3_"""    ):
            lowerCamelCase					:					int        =	512
            lowerCamelCase					:					Union[str, Any]        =	16
            lowerCamelCase					:					Tuple        =	21
            lowerCamelCase					:					Dict        =	"""pascal-voc-id2label.json"""
      else:
            lowerCamelCase					:					List[str]        =	1000
            lowerCamelCase					:					List[str]        =	"""imagenet-1k-id2label.json"""

      lowerCamelCase					:					Optional[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Union[str, Any]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					List[Any]        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
      lowerCamelCase					:					Union[str, Any]        =	idalabel
      lowerCamelCase					:					Union[str, Any]        =	{v: k for k, v in idalabel.items()}

      return config




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						Optional[Any]=False    )   ->      Optional[int]:
      for i in range(1			,				6    ):
            if F'layer_{i}.' in name:
                  lowerCamelCase					:					Optional[int]        =	name.replace(F'layer_{i}.'			,				F'encoder.layer.{i - 1}.'    )

      if "conv_1." in name:
            lowerCamelCase					:					Union[str, Any]        =	name.replace("""conv_1."""			,				"""conv_stem."""    )
      if ".block." in name:
            lowerCamelCase					:					Tuple        =	name.replace(""".block."""			,				"""."""    )

      if "exp_1x1" in name:
            lowerCamelCase					:					int        =	name.replace("""exp_1x1"""			,				"""expand_1x1"""    )
      if "red_1x1" in name:
            lowerCamelCase					:					List[str]        =	name.replace("""red_1x1"""			,				"""reduce_1x1"""    )
      if ".local_rep.conv_3x3." in name:
            lowerCamelCase					:					Any        =	name.replace(""".local_rep.conv_3x3."""			,				""".conv_kxk."""    )
      if ".local_rep.conv_1x1." in name:
            lowerCamelCase					:					Union[str, Any]        =	name.replace(""".local_rep.conv_1x1."""			,				""".conv_1x1."""    )
      if ".norm." in name:
            lowerCamelCase					:					List[Any]        =	name.replace(""".norm."""			,				""".normalization."""    )
      if ".conv." in name:
            lowerCamelCase					:					Dict        =	name.replace(""".conv."""			,				""".convolution."""    )
      if ".conv_proj." in name:
            lowerCamelCase					:					Dict        =	name.replace(""".conv_proj."""			,				""".conv_projection."""    )

      for i in range(0			,				2    ):
            for j in range(0			,				4    ):
                  if F'.{i}.{j}.' in name:
                        lowerCamelCase					:					Tuple        =	name.replace(F'.{i}.{j}.'			,				F'.{i}.layer.{j}.'    )

      for i in range(2			,				6    ):
            for j in range(0			,				4    ):
                  if F'.{i}.{j}.' in name:
                        lowerCamelCase					:					Union[str, Any]        =	name.replace(F'.{i}.{j}.'			,				F'.{i}.'    )
                        if "expand_1x1" in name:
                              lowerCamelCase					:					Optional[int]        =	name.replace("""expand_1x1"""			,				"""downsampling_layer.expand_1x1"""    )
                        if "conv_3x3" in name:
                              lowerCamelCase					:					Tuple        =	name.replace("""conv_3x3"""			,				"""downsampling_layer.conv_3x3"""    )
                        if "reduce_1x1" in name:
                              lowerCamelCase					:					List[str]        =	name.replace("""reduce_1x1"""			,				"""downsampling_layer.reduce_1x1"""    )

      for i in range(2			,				5    ):
            if F'.global_rep.{i}.weight' in name:
                  lowerCamelCase					:					str        =	name.replace(F'.global_rep.{i}.weight'			,				""".layernorm.weight"""    )
            if F'.global_rep.{i}.bias' in name:
                  lowerCamelCase					:					Union[str, Any]        =	name.replace(F'.global_rep.{i}.bias'			,				""".layernorm.bias"""    )

      if ".global_rep." in name:
            lowerCamelCase					:					Optional[Any]        =	name.replace(""".global_rep."""			,				""".transformer."""    )
      if ".pre_norm_mha.0." in name:
            lowerCamelCase					:					Optional[Any]        =	name.replace(""".pre_norm_mha.0."""			,				""".layernorm_before."""    )
      if ".pre_norm_mha.1.out_proj." in name:
            lowerCamelCase					:					Optional[int]        =	name.replace(""".pre_norm_mha.1.out_proj."""			,				""".attention.output.dense."""    )
      if ".pre_norm_ffn.0." in name:
            lowerCamelCase					:					Any        =	name.replace(""".pre_norm_ffn.0."""			,				""".layernorm_after."""    )
      if ".pre_norm_ffn.1." in name:
            lowerCamelCase					:					Any        =	name.replace(""".pre_norm_ffn.1."""			,				""".intermediate.dense."""    )
      if ".pre_norm_ffn.4." in name:
            lowerCamelCase					:					int        =	name.replace(""".pre_norm_ffn.4."""			,				""".output.dense."""    )
      if ".transformer." in name:
            lowerCamelCase					:					Any        =	name.replace(""".transformer."""			,				""".transformer.layer."""    )

      if ".aspp_layer." in name:
            lowerCamelCase					:					str        =	name.replace(""".aspp_layer."""			,				"""."""    )
      if ".aspp_pool." in name:
            lowerCamelCase					:					Tuple        =	name.replace(""".aspp_pool."""			,				"""."""    )
      if "seg_head." in name:
            lowerCamelCase					:					Dict        =	name.replace("""seg_head."""			,				"""segmentation_head."""    )
      if "segmentation_head.classifier.classifier." in name:
            lowerCamelCase					:					Tuple        =	name.replace("""segmentation_head.classifier.classifier."""			,				"""segmentation_head.classifier."""    )

      if "classifier.fc." in name:
            lowerCamelCase					:					Dict        =	name.replace("""classifier.fc."""			,				"""classifier."""    )
      elif (not base_model) and ("segmentation_head." not in name):
            lowerCamelCase					:					str        =	"""mobilevit.""" + name

      return name




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						str=False    )   ->      Optional[Any]:
      if base_model:
            lowerCamelCase					:					str        =	""""""
      else:
            lowerCamelCase					:					Union[str, Any]        =	"""mobilevit."""

      for key in orig_state_dict.copy().keys():
            lowerCamelCase					:					List[str]        =	orig_state_dict.pop(UpperCamelCase__    )

            if key[:8] == "encoder.":
                  lowerCamelCase					:					Union[str, Any]        =	key[8:]

            if "qkv" in key:
                  lowerCamelCase					:					Dict        =	key.split("""."""    )
                  lowerCamelCase					:					List[Any]        =	int(key_split[0][6:]    ) - 1
                  lowerCamelCase					:					Dict        =	int(key_split[3]    )
                  lowerCamelCase					:					str        =	model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}'    )
                  lowerCamelCase					:					int        =	layer.transformer.layer[transformer_num].attention.attention.all_head_size
                  lowerCamelCase					:					Dict        =	(
                      F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
                  )
                  if "weight" in key:
                        lowerCamelCase					:					Dict        =	val[:dim, :]
                        lowerCamelCase					:					List[Any]        =	val[dim : dim * 2, :]
                        lowerCamelCase					:					Optional[int]        =	val[-dim:, :]
                  else:
                        lowerCamelCase					:					Union[str, Any]        =	val[:dim]
                        lowerCamelCase					:					Any        =	val[dim : dim * 2]
                        lowerCamelCase					:					str        =	val[-dim:]
            else:
                  lowerCamelCase					:					Union[str, Any]        =	val

      return orig_state_dict




def   snake_case		(     )   ->      int:
      lowerCamelCase					:					Optional[int]        =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCamelCase					:					int        =	Image.open(requests.get(UpperCamelCase__			,				stream=UpperCamelCase__    ).raw    )
      return im




@torch.no_grad()
def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						int=False    )   ->      str:
      lowerCamelCase					:					List[Any]        =	get_mobilevit_config(UpperCamelCase__    )

      # load original state_dict
      lowerCamelCase					:					str        =	torch.load(UpperCamelCase__			,				map_location="""cpu"""    )

      # load 🤗 model
      if mobilevit_name.startswith("""deeplabv3_"""    ):
            lowerCamelCase					:					Union[str, Any]        =	MobileViTForSemanticSegmentation(UpperCamelCase__    ).eval()
      else:
            lowerCamelCase					:					Any        =	MobileViTForImageClassification(UpperCamelCase__    ).eval()

      lowerCamelCase					:					Optional[Any]        =	convert_state_dict(UpperCamelCase__			,				UpperCamelCase__    )
      model.load_state_dict(UpperCamelCase__    )

      # Check outputs on an image, prepared by MobileViTImageProcessor
      lowerCamelCase					:					Tuple        =	MobileViTImageProcessor(crop_size=config.image_size			,				size=config.image_size + 32    )
      lowerCamelCase					:					Optional[int]        =	image_processor(images=prepare_img()			,				return_tensors="""pt"""    )
      lowerCamelCase					:					Optional[int]        =	model(**UpperCamelCase__    )
      lowerCamelCase					:					int        =	outputs.logits

      if mobilevit_name.startswith("""deeplabv3_"""    ):
            assert logits.shape == (1, 21, 32, 32)

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  lowerCamelCase					:					List[Any]        =	torch.tensor(
                      [
                          [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                          [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                          [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                      ]    )
            elif mobilevit_name == "deeplabv3_mobilevit_xs":
                  lowerCamelCase					:					List[Any]        =	torch.tensor(
                      [
                          [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                          [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                          [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                      ]    )
            elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                  lowerCamelCase					:					Dict        =	torch.tensor(
                      [
                          [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                          [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                          [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                      ]    )
            else:
                  raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'    )

            assert torch.allclose(logits[0, :3, :3, :3]			,				UpperCamelCase__			,				atol=1E-4    )
      else:
            assert logits.shape == (1, 1000)

            if mobilevit_name == "mobilevit_s":
                  lowerCamelCase					:					Dict        =	torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]    )
            elif mobilevit_name == "mobilevit_xs":
                  lowerCamelCase					:					Tuple        =	torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]    )
            elif mobilevit_name == "mobilevit_xxs":
                  lowerCamelCase					:					Dict        =	torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]    )
            else:
                  raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'    )

            assert torch.allclose(logits[0, :3]			,				UpperCamelCase__			,				atol=1E-4    )

      Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
      print(F'Saving model {mobilevit_name} 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:
            lowerCamelCase					:					Optional[int]        =	{
                """mobilevit_s""": """mobilevit-small""",
                """mobilevit_xs""": """mobilevit-x-small""",
                """mobilevit_xxs""": """mobilevit-xx-small""",
                """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
                """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
                """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
            }

            print("""Pushing to the hub..."""    )
            lowerCamelCase					:					Optional[Any]        =	model_mapping[mobilevit_name]
            image_processor.push_to_hub(UpperCamelCase__			,				organization="""apple"""    )
            model.push_to_hub(UpperCamelCase__			,				organization="""apple"""    )


if __name__ == "__main__":
    __lowerCamelCase      :str	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--mobilevit_name',
        default='mobilevit_s',
        type=str,
        help=(
            'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
            ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
        ),
    )
    parser.add_argument(
        '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    __lowerCamelCase      :int	    =		parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )








"""simple docstring"""


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 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Tuple     ,		__a:						Optional[int]	)->    List[str]:
           return None


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Tuple     ,		__a:						str     ,		__a:						str     ,		__a:						str	)->    Tuple:
           return None


class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=[
         # (model_name, model_kwargs)
         ('''bert-base-cased''', {}),
         ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
     ]

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						str	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						Union[str, Any]	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
           with NamedTemporaryFile(mode="""w+t"""	) as vocab_file:
                 vocab_file.write("""\n""".join(__a	)	)
                 vocab_file.flush()
                 lowerCamelCase					:					Dict        =	BertTokenizerFast(vocab_file.name	)

           with TemporaryDirectory() as bert_save_dir:
                 lowerCamelCase					:					List[str]        =	BertModel(BertConfig(vocab_size=len(__a	)	)	)
                 model.save_pretrained(__a	)
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		__a	)

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Optional[int]        =	self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Tuple        =	quantize(Path(__a	)	)

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

     @require_torch
     @slow
     def 		a__						(	self:						Any	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Any        =	self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Dict        =	quantize(__a	)

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

     def 		a__						(	self:						List[Any]     ,		__a:						Optional[Any]     ,		__a:						List[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]=None     ,		**__a:						Optional[int]	)->    Any:
           try:
                 # Compute path
                 with TemporaryDirectory() as tempdir:
                       lowerCamelCase					:					Optional[Any]        =	Path(__a	).joinpath("""model.onnx"""	)

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

                 # Export
                 convert(__a     ,		__a     ,		__a     ,		__a     ,		__a     ,		**__a	)

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

     @require_torch
     @require_tokenizers
     @slow
     def 		a__						(	self:						Tuple	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
           lowerCamelCase					:					List[Any]        =	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
           self._test_infer_dynamic_axis(__a     ,		__a     ,		"""pt"""	)

     @require_tf
     @require_tokenizers
     @slow
     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           from transformers import TFBertModel

           lowerCamelCase					:					Union[str, Any]        =	TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
           lowerCamelCase					:					str        =	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
           self._test_infer_dynamic_axis(__a     ,		__a     ,		"""tf"""	)

     def 		a__						(	self:						List[str]     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						str	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	FeatureExtractionPipeline(__a     ,		__a	)

           lowerCamelCase					:					List[str]        =	["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	infer_shapes(__a     ,		__a	)

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

           # 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 		a__						(	self:						List[Any]	)->    int:
           lowerCamelCase					:					List[str]        =	["""input_ids""", """attention_mask""", """token_type_ids"""]
           lowerCamelCase					:					str        =	{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncContiguousArgs()     ,		__a     ,		__a	)

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

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

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

           # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncNonContiguousArgs()     ,		__a     ,		__a	)

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

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







     def 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					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()	)




