


"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertConfig,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertWithLMHeadModel,
    )


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Union[str, Any]     ,		__a:						Any     ,		)->    List[Any]:
           lowerCamelCase					:					Dict        =	parent
           lowerCamelCase					:					List[str]        =	13
           lowerCamelCase					:					Optional[int]        =	7
           lowerCamelCase					:					str        =	True
           lowerCamelCase					:					Optional[Any]        =	True
           lowerCamelCase					:					Tuple        =	True
           lowerCamelCase					:					Optional[Any]        =	True
           lowerCamelCase					:					str        =	True
           lowerCamelCase					:					int        =	False
           lowerCamelCase					:					Any        =	False
           lowerCamelCase					:					Union[str, Any]        =	False
           lowerCamelCase					:					Tuple        =	2
           lowerCamelCase					:					Union[str, Any]        =	99
           lowerCamelCase					:					int        =	0
           lowerCamelCase					:					int        =	32
           lowerCamelCase					:					str        =	2
           lowerCamelCase					:					Optional[Any]        =	4
           lowerCamelCase					:					Optional[Any]        =	0.1
           lowerCamelCase					:					Optional[int]        =	0.1
           lowerCamelCase					:					List[Any]        =	512
           lowerCamelCase					:					Optional[int]        =	16
           lowerCamelCase					:					Optional[Any]        =	2
           lowerCamelCase					:					int        =	0.02
           lowerCamelCase					:					Union[str, Any]        =	3
           lowerCamelCase					:					List[Any]        =	4
           lowerCamelCase					:					List[str]        =	"""last"""
           lowerCamelCase					:					List[Any]        =	True
           lowerCamelCase					:					Optional[Any]        =	None
           lowerCamelCase					:					str        =	0

     def 		a__						(	self:						int	)->    Tuple:
           lowerCamelCase					:					Dict        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)
           lowerCamelCase					:					Optional[int]        =	random_attention_mask([self.batch_size, self.seq_length]     ,		dtype=tf.floataa	)

           lowerCamelCase					:					Tuple        =	None
           if self.use_input_lengths:
                 lowerCamelCase					:					Optional[Any]        =	(
                     ids_tensor([self.batch_size]     ,		vocab_size=2	) + self.seq_length - 2
                 )  # small variation of seq_length

           lowerCamelCase					:					Dict        =	None
           if self.use_token_type_ids:
                 lowerCamelCase					:					Union[str, Any]        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.n_langs	)

           lowerCamelCase					:					Optional[Any]        =	None
           lowerCamelCase					:					Dict        =	None
           lowerCamelCase					:					Any        =	None
           if self.use_labels:
                 lowerCamelCase					:					List[Any]        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)
                 lowerCamelCase					:					Tuple        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels	)
                 lowerCamelCase					:					Optional[Any]        =	ids_tensor([self.batch_size]     ,		2     ,		dtype=tf.floataa	)
                 lowerCamelCase					:					str        =	ids_tensor([self.batch_size]     ,		self.num_choices	)

           lowerCamelCase					:					Dict        =	FlaubertConfig(
               vocab_size=self.vocab_size     ,		n_special=self.n_special     ,		emb_dim=self.hidden_size     ,		n_layers=self.num_hidden_layers     ,		n_heads=self.num_attention_heads     ,		dropout=self.hidden_dropout_prob     ,		attention_dropout=self.attention_probs_dropout_prob     ,		gelu_activation=self.gelu_activation     ,		sinusoidal_embeddings=self.sinusoidal_embeddings     ,		asm=self.asm     ,		causal=self.causal     ,		n_langs=self.n_langs     ,		max_position_embeddings=self.max_position_embeddings     ,		initializer_range=self.initializer_range     ,		summary_type=self.summary_type     ,		use_proj=self.use_proj     ,		bos_token_id=self.bos_token_id     ,		)

           return (
               config,
               input_ids,
               token_type_ids,
               input_lengths,
               sequence_labels,
               token_labels,
               is_impossible_labels,
               choice_labels,
               input_mask,
           )

     def 		a__						(	self:						Tuple     ,		__a:						Optional[int]     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]     ,		__a:						Tuple     ,		__a:						int     ,		__a:						Tuple     ,		__a:						Optional[int]     ,		)->    Optional[Any]:
           lowerCamelCase					:					Union[str, Any]        =	TFFlaubertModel(config=_SCREAMING_SNAKE_CASE	)
           lowerCamelCase					:					Tuple        =	{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowerCamelCase					:					Tuple        =	model(_SCREAMING_SNAKE_CASE	)

           lowerCamelCase					:					Union[str, Any]        =	[input_ids, input_mask]
           lowerCamelCase					:					List[Any]        =	model(_SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Any     ,		__a:						Tuple     ,		__a:						str     ,		__a:						List[Any]     ,		__a:						int     ,		__a:						Dict     ,		__a:						Dict     ,		__a:						Tuple     ,		__a:						int     ,		)->    Tuple:
           lowerCamelCase					:					Any        =	TFFlaubertWithLMHeadModel(_SCREAMING_SNAKE_CASE	)

           lowerCamelCase					:					Optional[int]        =	{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
           lowerCamelCase					:					List[str]        =	model(_SCREAMING_SNAKE_CASE	)

           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						str     ,		__a:						Union[str, Any]     ,		__a:						Optional[int]     ,		__a:						List[Any]     ,		__a:						List[str]     ,		__a:						Optional[Any]     ,		__a:						Tuple     ,		__a:						Dict     ,		__a:						str     ,		)->    Tuple:
           lowerCamelCase					:					Any        =	TFFlaubertForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE	)

           lowerCamelCase					:					Union[str, Any]        =	{"""input_ids""": input_ids, """lengths""": input_lengths}

           lowerCamelCase					:					Union[str, Any]        =	model(_SCREAMING_SNAKE_CASE	)

           self.parent.assertEqual(result.start_logits.shape     ,		(self.batch_size, self.seq_length)	)
           self.parent.assertEqual(result.end_logits.shape     ,		(self.batch_size, self.seq_length)	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[Any]     ,		__a:						str     ,		__a:						int     ,		__a:						Optional[Any]     ,		__a:						int     ,		__a:						Tuple     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						Optional[int]     ,		)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	TFFlaubertForSequenceClassification(_SCREAMING_SNAKE_CASE	)

           lowerCamelCase					:					List[Any]        =	{"""input_ids""": input_ids, """lengths""": input_lengths}

           lowerCamelCase					:					Dict        =	model(_SCREAMING_SNAKE_CASE	)

           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)

     def 		a__						(	self:						Any     ,		__a:						List[str]     ,		__a:						List[str]     ,		__a:						List[Any]     ,		__a:						List[str]     ,		__a:						str     ,		__a:						List[str]     ,		__a:						str     ,		__a:						List[Any]     ,		__a:						str     ,		)->    Tuple:
           lowerCamelCase					:					int        =	self.num_labels
           lowerCamelCase					:					Dict        =	TFFlaubertForTokenClassification(config=_SCREAMING_SNAKE_CASE	)
           lowerCamelCase					:					Optional[Any]        =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
           lowerCamelCase					:					Optional[Any]        =	model(_SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels)	)

     def 		a__						(	self:						List[Any]     ,		__a:						List[str]     ,		__a:						List[Any]     ,		__a:						str     ,		__a:						int     ,		__a:						Any     ,		__a:						List[Any]     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						Dict     ,		)->    Any:
           lowerCamelCase					:					Union[str, Any]        =	self.num_choices
           lowerCamelCase					:					Any        =	TFFlaubertForMultipleChoice(config=_SCREAMING_SNAKE_CASE	)
           lowerCamelCase					:					Dict        =	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE     ,		1	)     ,		(1, self.num_choices, 1)	)
           lowerCamelCase					:					List[Any]        =	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE     ,		1	)     ,		(1, self.num_choices, 1)	)
           lowerCamelCase					:					Tuple        =	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE     ,		1	)     ,		(1, self.num_choices, 1)	)
           lowerCamelCase					:					str        =	{
               """input_ids""": multiple_choice_inputs_ids,
               """attention_mask""": multiple_choice_input_mask,
               """token_type_ids""": multiple_choice_token_type_ids,
           }
           lowerCamelCase					:					Tuple        =	model(_SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices)	)







     def 		a__						(	self:						int	)->    int:
           lowerCamelCase					:					Optional[Any]        =	self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,			(
               lowerCamelCase		
           )     ,					
           )					:					Any        =	config_and_inputs
           lowerCamelCase					:					Any        =	{
               """input_ids""": input_ids,
               """token_type_ids""": token_type_ids,
               """langs""": token_type_ids,
               """lengths""": input_lengths,
           }
           return config, inputs_dict


@require_tf
class 			A__	(						snake_case__ ,     snake_case__ ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=(
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     snake_case__	:					str													=(
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     snake_case__	:					Tuple													=(
         {
             '''feature-extraction''': TFFlaubertModel,
             '''fill-mask''': TFFlaubertWithLMHeadModel,
             '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
             '''text-classification''': TFFlaubertForSequenceClassification,
             '''token-classification''': TFFlaubertForTokenClassification,
             '''zero-shot''': TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     snake_case__	:					Any													=False
     snake_case__	:					Optional[int]													=False

     def 		a__						(	self:						str     ,		__a:						Union[str, Any]     ,		__a:						int     ,		__a:						Optional[Any]     ,		__a:						Tuple     ,		__a:						List[Any]	)->    Optional[int]:
           if (
               pipeline_test_casse_name == "QAPipelineTests"
               and tokenizer_name is not None
               and not tokenizer_name.endswith("""Fast"""	)
           ):
                 # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                 # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                 # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                 return True

           return False

     def 		a__						(	self:						List[Any]	)->    Dict:
           lowerCamelCase					:					Tuple        =	TFFlaubertModelTester(self	)
           lowerCamelCase					:					Union[str, Any]        =	ConfigTester(self     ,		config_class=_SCREAMING_SNAKE_CASE     ,		emb_dim=37	)

     def 		a__						(	self:						Union[str, Any]	)->    Union[str, Any]:
           self.config_tester.run_common_tests()

     def 		a__						(	self:						Optional[int]	)->    Optional[int]:
           lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*_SCREAMING_SNAKE_CASE	)

     def 		a__						(	self:						List[Any]	)->    List[str]:
           lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*_SCREAMING_SNAKE_CASE	)

     def 		a__						(	self:						Tuple	)->    Optional[Any]:
           lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*_SCREAMING_SNAKE_CASE	)

     def 		a__						(	self:						Any	)->    Any:
           lowerCamelCase					:					Optional[int]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*_SCREAMING_SNAKE_CASE	)

     def 		a__						(	self:						List[Any]	)->    int:
           lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_token_classification(*_SCREAMING_SNAKE_CASE	)

     def 		a__						(	self:						List[str]	)->    str:
           lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_multiple_choice(*_SCREAMING_SNAKE_CASE	)







     @slow
     def 		a__						(	self:						List[Any]	)->    Optional[int]:
           for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					Optional[int]        =	TFFlaubertModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)


@require_tf
@require_sentencepiece
@require_tokenizers
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @slow
     def 		a__						(	self:						Union[str, Any]	)->    Any:
           lowerCamelCase					:					Union[str, Any]        =	TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""	)

           lowerCamelCase					:					List[str]        =	tf.convert_to_tensor(
               [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]     ,		dtype=tf.intaa     ,		)  # "J'aime flaubert !"

           lowerCamelCase					:					List[Any]        =	model(_SCREAMING_SNAKE_CASE	)[0]
           lowerCamelCase					:					List[str]        =	tf.TensorShape((1, 8, 512)	)
           self.assertEqual(output.shape     ,		_SCREAMING_SNAKE_CASE	)
           # compare the actual values for a slice.
           lowerCamelCase					:					Optional[int]        =	tf.convert_to_tensor(
               [
                   [
                       [-1.8_76_87_73, -1.56_65_55, 0.27_07_24_18],
                       [-1.6_92_00_38, -0.5_87_35_05, 1.9_32_95_99],
                       [-2.9_56_39_85, -1.6_99_38_35, 1.7_97_20_52],
                   ]
               ]     ,		dtype=tf.floataa     ,		)

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()     ,		expected_slice.numpy()     ,		atol=1e-4	)	)






"""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()	)




