
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        _UpperCamelCase				:






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Any						,	snake_case_ :       int      ):
							return None




class        _UpperCamelCase				:






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       Dict						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       List[Any]      ):
							return None




class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						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       lowerCAmelCase__	(       self :       Optional[int]      ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(lowercase_						,	"""tf"""						,	12						,	**lowercase_      )

			@require_torch
			@slow
			def       lowerCAmelCase__	(       self :       Tuple      ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(lowercase_						,	"""pt"""						,	12						,	**lowercase_      )

			@require_torch
			@slow
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							from transformers import BertModel

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

							with TemporaryDirectory() as bert_save_dir:
											UpperCamelCase_:							Any	=						BertModel(BertConfig(vocab_size=len(lowercase_      )      )      )
											model.save_pretrained(lowercase_      )
											self._test_export(lowercase_						,	"""pt"""						,	12						,	lowercase_      )

			@require_tf
			@slow
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											UpperCamelCase_:							Optional[int]	=						self._test_export(lowercase_						,	"""tf"""						,	12						,	**lowercase_      )
											UpperCamelCase_:							Dict	=						quantize(Path(lowercase_      )      )

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

			@require_torch
			@slow
			def       lowerCAmelCase__	(       self :       Any      ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											UpperCamelCase_:							Union[str, Any]	=						self._test_export(lowercase_						,	"""pt"""						,	12						,	**lowercase_      )
											UpperCamelCase_:							List[Any]	=						quantize(lowercase_      )

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

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       List[str]						,	snake_case_ :       int						,	snake_case_ :       List[str]						,	snake_case_ :       List[str]=None						,	**snake_case_ :       Tuple      ):
							try:
											# Compute path
											with TemporaryDirectory() as tempdir:
															UpperCamelCase_:							List[str]	=						Path(lowercase_      ).joinpath("""model.onnx"""      )

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

											# Export
											convert(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_      )

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

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

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

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

							UpperCamelCase_:							Any	=						TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""      )      )
							UpperCamelCase_:							str	=						BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""      )
							self._test_infer_dynamic_axis(lowercase_						,	lowercase_						,	"""tf"""      )

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       Tuple						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							Optional[int]	=						FeatureExtractionPipeline(lowercase_						,	lowercase_      )

							UpperCamelCase_:							List[Any]	=						["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
							UpperCamelCase_:							Any	=						infer_shapes(lowercase_						,	lowercase_      )

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

							# 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 :       Tuple      ):
							UpperCamelCase_:							int	=						["""input_ids""", """attention_mask""", """token_type_ids"""]
							UpperCamelCase_:							List[Any]	=						{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
							UpperCamelCase_:							Optional[Any]	=						ensure_valid_input(FuncContiguousArgs()						,	lowercase_						,	lowercase_      )

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

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

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

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

							# Should have exactly the one arg (all before the one not provided "some_other_args")
							self.assertEqual(len(lowercase_      )						,	1      )
							self.assertEqual(len(lowercase_      )						,	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      ):
							UpperCamelCase_:							Tuple	=						generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""      )						,	"""-test"""      )
							self.assertEqual("""/home/something/my_fake_model-test.onnx"""						,	generated.as_posix()      )








import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = BarthezTokenizer
			__UpperCamelCase      :						str					      = BarthezTokenizerFast
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						List[Any]					      = True

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							super().setUp()

							UpperCamelCase_:							Tuple	=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""      )
							tokenizer.save_pretrained(self.tmpdirname      )
							tokenizer.save_pretrained(self.tmpdirname						,	legacy_format=snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							str	=						"""<pad>"""
							UpperCamelCase_:							int	=						1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_      )						,	snake_case_      )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_      )						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0]						,	"""<s>"""      )
							self.assertEqual(vocab_keys[1]						,	"""<pad>"""      )
							self.assertEqual(vocab_keys[-1]						,	"""<mask>"""      )
							self.assertEqual(len(snake_case_      )						,	10_1122      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							self.assertEqual(self.get_tokenizer().vocab_size						,	10_1122      )

			@require_torch
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:							Union[str, Any]	=						[0, 57, 3018, 7_0307, 91, 2]

							UpperCamelCase_:							Union[str, Any]	=						self.tokenizer(
							    snake_case_						,	max_length=len(snake_case_      )						,	padding=snake_case_						,	truncation=snake_case_						,	return_tensors="""pt"""      )
							self.assertIsInstance(snake_case_						,	snake_case_      )

							self.assertEqual((2, 6)						,	batch.input_ids.shape      )
							self.assertEqual((2, 6)						,	batch.attention_mask.shape      )
							UpperCamelCase_:							Any	=						batch.input_ids.tolist()[0]
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Union[str, Any]	=						self.get_rust_tokenizer()

							UpperCamelCase_:							str	=						"""I was born in 92000, and this is falsé."""

							UpperCamelCase_:							str	=						tokenizer.tokenize(snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.tokenize(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							int	=						tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							List[str]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Tuple	=						tokenizer.encode(snake_case_      )
							UpperCamelCase_:							Tuple	=						rust_tokenizer.encode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       int      ):
							# fmt: off
							UpperCamelCase_:							Optional[Any]	=						{"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# moussaKam/mbarthez is a french model. So we also use french texts.
							UpperCamelCase_:							str	=						[
							    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
							    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
							    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
							    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
							    """telles que la traduction et la synthèse de texte.""",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case_						,	model_name="""moussaKam/mbarthez"""						,	revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,	sequences=snake_case_						,	)






