"""simple docstring"""





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, 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_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           LevitForImageClassification,
           LevitForImageClassificationWithTeacher,
           LevitModel,
       )
       from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import LevitImageProcessor


class      UpperCAmelCase_	(    __lowerCamelCase):


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[int]       =	self.config_class(**self.inputs_dict							)
             self.parent.assertTrue(hasattr(a		,			'hidden_sizes'							)							)
             self.parent.assertTrue(hasattr(a		,			'num_attention_heads'							)							)





class      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_3		,			a=6_4		,			a=3		,			a=3		,			a=2		,			a=1		,			a=1_6		,			a=[1_2_8, 2_5_6, 3_8_4]		,			a=[4, 6, 8]		,			a=[2, 3, 4]		,			a=[1_6, 1_6, 1_6]		,			a=0		,			a=[2, 2, 2]		,			a=[2, 2, 2]		,			a=0.02		,			a=True		,			a=True		,			a=2		,			)       ->					Tuple:
             lowercase__			:		Optional[int]       =	parent
             lowercase__			:		Any       =	batch_size
             lowercase__			:		Tuple       =	image_size
             lowercase__			:		List[str]       =	num_channels
             lowercase__			:		str       =	kernel_size
             lowercase__			:		Dict       =	stride
             lowercase__			:		Tuple       =	padding
             lowercase__			:		Dict       =	hidden_sizes
             lowercase__			:		Union[str, Any]       =	num_attention_heads
             lowercase__			:		int       =	depths
             lowercase__			:		List[Any]       =	key_dim
             lowercase__			:		List[Any]       =	drop_path_rate
             lowercase__			:		Tuple       =	patch_size
             lowercase__			:		Union[str, Any]       =	attention_ratio
             lowercase__			:		str       =	mlp_ratio
             lowercase__			:		int       =	initializer_range
             lowercase__			:		str       =	[
                 ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                 ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
             ]
             lowercase__			:		Union[str, Any]       =	is_training
             lowercase__			:		int       =	use_labels
             lowercase__			:		str       =	num_labels
             lowercase__			:		Optional[Any]       =	initializer_range


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		str       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__			:		Optional[int]       =	None
             if self.use_labels:
                    lowercase__			:		List[str]       =	ids_tensor([self.batch_size]		,			self.num_labels							)

             lowercase__			:		Optional[Any]       =	self.get_config()
             return config, pixel_values, labels


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             return LevitConfig(
                 image_size=self.image_size		,			num_channels=self.num_channels		,			kernel_size=self.kernel_size		,			stride=self.stride		,			padding=self.padding		,			patch_size=self.patch_size		,			hidden_sizes=self.hidden_sizes		,			num_attention_heads=self.num_attention_heads		,			depths=self.depths		,			key_dim=self.key_dim		,			drop_path_rate=self.drop_path_rate		,			mlp_ratio=self.mlp_ratio		,			attention_ratio=self.attention_ratio		,			initializer_range=self.initializer_range		,			down_ops=self.down_ops		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					str:
             lowercase__			:		Union[str, Any]       =	LevitModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__			:		Any       =	model(a							)
             lowercase__			:		Optional[int]       =	(self.image_size, self.image_size)
             lowercase__    ,      lowercase__			:		Dict       =	image_size[0], image_size[1]
             for _ in range(4							):
                    lowercase__			:		Optional[Any]       =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
                    lowercase__			:		Union[str, Any]       =	floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
             self.parent.assertEqual(
                 result.last_hidden_state.shape		,			(self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[str]:
             lowercase__			:		Optional[int]       =	self.num_labels
             lowercase__			:		List[Any]       =	LevitForImageClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__			:		List[Any]       =	model(a		,			labels=a							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		int       =	self.prepare_config_and_inputs()
             lowercase__    ,      lowercase__    ,      lowercase__			:		Any       =	config_and_inputs
             lowercase__			:		Dict       =	{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class      UpperCAmelCase_	(    __lowerCamelCase ,							__lowerCamelCase ,							unittest.TestCase):
      lowerCamelCase__		:    Tuple   =  (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      lowerCamelCase__		:    Dict   =  (
          {
              """feature-extraction""": LevitModel,
              """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      lowerCamelCase__		:    Dict   =  False
      lowerCamelCase__		:    List[str]   =  False
      lowerCamelCase__		:    List[str]   =  False
      lowerCamelCase__		:    str   =  False
      lowerCamelCase__		:    List[str]   =  False


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		Any       =	LevitModelTester(self							)
             lowercase__			:		int       =	ConfigTester(self		,			config_class=a		,			has_text_modality=a		,			hidden_size=3_7							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             self.create_and_test_config_common_properties()
             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             return


      @unittest.skip(reason='Levit does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					Any:
             pass


      @unittest.skip(reason='Levit does not support input and output embeddings'							)
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             pass


      @unittest.skip(reason='Levit does not output attentions'							)
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__    ,      lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		Any       =	model_class(a							)
                    lowercase__			:		Union[str, Any]       =	inspect.signature(model.forward							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__			:		int       =	[*signature.parameters.keys()]

                    lowercase__			:		List[Any]       =	['pixel_values']
                    self.assertListEqual(arg_names[:1]		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             def check_hidden_states_output(a		,			a		,			a							):
                    lowercase__			:		int       =	model_class(a							)
                    model.to(a							)
                    model.eval()

                    with torch.no_grad():
                           lowercase__			:		str       =	model(**self._prepare_for_class(a		,			a							)							)

                    lowercase__			:		int       =	outputs.hidden_states

                    lowercase__			:		Union[str, Any]       =	len(self.model_tester.depths							) + 1
                    self.assertEqual(len(a							)		,			a							)

                    lowercase__			:		Any       =	(self.model_tester.image_size, self.model_tester.image_size)
                    lowercase__    ,      lowercase__			:		List[Any]       =	image_size[0], image_size[1]
                    for _ in range(4							):
                           lowercase__			:		List[Any]       =	floor(
                               (
                                   (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                   / self.model_tester.stride
                               )
                               + 1							)
                           lowercase__			:		List[Any]       =	floor(
                               (
                                   (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                   / self.model_tester.stride
                               )
                               + 1							)
                    # verify the first hidden states (first block)
                    self.assertListEqual(
                        list(hidden_states[0].shape[-2:]							)		,			[
                            height * width,
                            self.model_tester.hidden_sizes[0],
                        ]		,			)

             lowercase__    ,      lowercase__			:		Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		Optional[int]       =	True
                    check_hidden_states_output(a		,			a		,			a							)

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

                    check_hidden_states_output(a		,			a		,			a							)


      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             pass


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a=False							)       ->					Union[str, Any]:
             lowercase__			:		Tuple       =	super()._prepare_for_class(a		,			a		,			return_labels=a							)

             if return_labels:
                    if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                           del inputs_dict["labels"]

             return inputs_dict


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Any       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             if not self.model_tester.is_training:
                    return

             lowercase__    ,      lowercase__			:		str       =	self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__			:		Optional[Any]       =	True

             for model_class in self.all_model_classes:
                    # LevitForImageClassificationWithTeacher supports inference-only
                    if (
                        model_class in get_values(a							)
                        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                    ):
                           continue
                    lowercase__			:		Union[str, Any]       =	model_class(a							)
                    model.to(a							)
                    model.train()
                    lowercase__			:		Union[str, Any]       =	self._prepare_for_class(a		,			a		,			return_labels=a							)
                    lowercase__			:		Optional[Any]       =	model(**a							).loss
                    loss.backward()


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__    ,      lowercase__			:		List[str]       =	self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             lowercase__			:		Optional[int]       =	False
             lowercase__			:		Dict       =	True

             for model_class in self.all_model_classes:
                    if model_class in get_values(a							) or not model_class.supports_gradient_checkpointing:
                           continue
                    # LevitForImageClassificationWithTeacher supports inference-only
                    if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                           continue
                    lowercase__			:		Optional[Any]       =	model_class(a							)
                    model.gradient_checkpointing_enable()
                    model.to(a							)
                    model.train()
                    lowercase__			:		List[Any]       =	self._prepare_for_class(a		,			a		,			return_labels=a							)
                    lowercase__			:		Any       =	model(**a							).loss
                    loss.backward()


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__    ,      lowercase__			:		int       =	self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__			:		Dict       =	[
                 {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
                 {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
                 {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
             ]

             for model_class in self.all_model_classes:
                    if (
                        model_class
                        not in [
                            *get_values(a							),
                        ]
                        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                    ):
                           continue

                    for problem_type in problem_types:
                           with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""							):
                                  lowercase__			:		str       =	problem_type['title']
                                  lowercase__			:		List[str]       =	problem_type['num_labels']

                                  lowercase__			:		int       =	model_class(a							)
                                  model.to(a							)
                                  model.train()

                                  lowercase__			:		List[Any]       =	self._prepare_for_class(a		,			a		,			return_labels=a							)

                                  if problem_type["num_labels"] > 1:
                                         lowercase__			:		List[Any]       =	inputs['labels'].unsqueeze(1							).repeat(1		,			problem_type['num_labels']							)

                                  lowercase__			:		Tuple       =	inputs['labels'].to(problem_type['dtype']							)

                                  # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                                  # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                                  # they have the same size." which is a symptom something in wrong for the regression problem.
                                  # See https://github.com/huggingface/transformers/issues/11780
                                  with warnings.catch_warnings(record=a							) as warning_list:
                                         lowercase__			:		Dict       =	model(**a							).loss
                                  for w in warning_list:
                                         if "Using a target size that is different to the input size" in str(w.message							):
                                                raise ValueError(
                                                    f"""Something is going wrong in the regression problem: intercepted {w.message}"""							)

                                  loss.backward()


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__			:		Dict       =	LevitModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)





def 				a_     (      ):





       '''simple docstring'''

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






@require_torch
@require_vision
class      UpperCAmelCase_	(    unittest.TestCase):


      @cached_property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Tuple       =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
                 a							)

             lowercase__			:		str       =	self.default_image_processor
             lowercase__			:		str       =	prepare_img()
             lowercase__			:		int       =	image_processor(images=a		,			return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__			:		Optional[int]       =	model(**a							)

             # verify the logits
             lowercase__			:		Optional[Any]       =	torch.Size((1, 1_0_0_0)							)
             self.assertEqual(outputs.logits.shape		,			a							)

             lowercase__			:		Union[str, Any]       =	torch.tensor([1.0_448, -0.3_745, -1.8_317]							).to(a							)

             self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			a		,			atol=1e-4							)							)



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 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


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





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

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

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





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )
			lowercase       =    quantize(Path(snake_case       )       )

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





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )
			lowercase       =    quantize(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case=None		,    **snake_case       ):
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				lowercase       =    Path(snake_case       ).joinpath('model.onnx'       )

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

			# Export
			convert(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    **snake_case       )

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





	@require_torch
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

		lowercase       =    BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'pt'       )





	@require_tf
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import TFBertModel

		lowercase       =    TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'tf'       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    FeatureExtractionPipeline(snake_case		,    snake_case       )

		lowercase       =    ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    infer_shapes(snake_case		,    snake_case       )

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

		# 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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['input_ids', 'attention_mask', 'token_type_ids']
		lowercase       =    {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
		lowercase  ,					lowercase       =    ensure_valid_input(FuncContiguousArgs()		,    snake_case		,    snake_case       )

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

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

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

		# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
		lowercase  ,					lowercase       =    ensure_valid_input(FuncNonContiguousArgs()		,    snake_case		,    snake_case       )

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    generate_identified_filename(Path('/home/something/my_fake_model.onnx'       )		,    '-test'       )
		self.assertEqual('/home/something/my_fake_model-test.onnx'		,    generated.as_posix()       )

