





'''simple docstring'''




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 			UpperCamelCase__ ( _a		, unittest.TestCase       ):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__            =     PhobertTokenizer
					SCREAMING_SNAKE_CASE__            =     False


					def lowerCamelCase_       (		self							:       Optional[int]		):

							'''simple docstring'''



							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							SCREAMING_SNAKE_CASE      :							int						=       ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
							SCREAMING_SNAKE_CASE      :							List[str]						=       dict(zip(__lowerCamelCase	,		range(len(__lowerCamelCase		)		)		)		)
							SCREAMING_SNAKE_CASE      :							Tuple						=       ["""#version: 0.2""", """l à</w>"""]
							SCREAMING_SNAKE_CASE      :							Dict						=       {"""unk_token""": """<unk>"""}

							SCREAMING_SNAKE_CASE      :							str						=       os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES["""vocab_file"""]		)
							SCREAMING_SNAKE_CASE      :							str						=       os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES["""merges_file"""]		)

							with open(self.vocab_file	,		"""w"""	,		encoding="""utf-8"""		) as fp:
									for token in vocab_tokens:
											fp.write(f'''{token} {vocab_tokens[token]}\n'''		)
							with open(self.merges_file	,		"""w"""	,		encoding="""utf-8"""		) as fp:
									fp.write("""\n""".join(__lowerCamelCase		)		)


					def lowerCamelCase_       (		self							:       List[Any]	,		**lowerCamelCase_							:       Optional[int]		):

							'''simple docstring'''



							kwargs.update(self.special_tokens_map		)
							return PhobertTokenizer.from_pretrained(self.tmpdirname	,		**__lowerCamelCase		)


					def lowerCamelCase_       (		self							:       Tuple	,		lowerCamelCase_							:       Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							List[str]						=       """Tôi là VinAI Research"""
							SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
							return input_text, output_text



					def lowerCamelCase_       (		self							:       Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							Any						=       PhobertTokenizer(self.vocab_file	,		self.merges_file	,		**self.special_tokens_map		)
							SCREAMING_SNAKE_CASE      :							int						=       """Tôi là VinAI Research"""
							SCREAMING_SNAKE_CASE      :							List[str]						=       """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
							SCREAMING_SNAKE_CASE      :							List[str]						=       tokenizer.tokenize(__lowerCamelCase		)
							print(__lowerCamelCase		)
							self.assertListEqual(__lowerCamelCase	,		__lowerCamelCase		)

							SCREAMING_SNAKE_CASE      :							Optional[Any]						=       tokens + [tokenizer.unk_token]

							SCREAMING_SNAKE_CASE      :							List[Any]						=       [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase		)	,		__lowerCamelCase		)











import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_tensorflow_text_available():
	from transformers.models.bert import TFBertTokenizer


UpperCAmelCase_ :						Optional[Any]      =      ['''bert-base-uncased''', '''bert-base-cased''']
UpperCAmelCase_ :						List[str]      =      '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():

	class     _SCREAMING_SNAKE_CASE       (			tf.keras.Model      ):





		def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any]	):
			super().__init__()
			UpperCamelCase     :Any          =	tokenizer
			UpperCamelCase     :List[str]          =	AutoConfig.from_pretrained(__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	TFAutoModel.from_config(__lowerCamelCase	)





		def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str	):
			UpperCamelCase     :str          =	self.tokenizer(__lowerCamelCase	)
			UpperCamelCase     :Any          =	self.bert(**__lowerCamelCase	)
			return out["pooler_output"]

@require_tf
@require_tensorflow_text
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def 						_A (		self				:					Dict	):
		super().setUp()

		UpperCamelCase     :int          =	[
		    BertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
		]  # repeat for when fast_bert_tokenizer=false
		UpperCamelCase     :Any          =	[TFBertTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in TOKENIZER_CHECKPOINTS] + [
		    TFBertTokenizer.from_pretrained(__lowerCamelCase ,  use_fast_bert_tokenizer=__lowerCamelCase	)
		    for checkpoint in TOKENIZER_CHECKPOINTS
		]
		assert len(self.tokenizers	) == len(self.tf_tokenizers	)

		UpperCamelCase     :Any          =	[
		    """This is a straightforward English test sentence.""",
		    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
		    """Now we're going to add some Chinese: 一 二 三 一二三""",
		    """And some much more rare Chinese: 齉 堃 齉堃""",
		    """Je vais aussi écrire en français pour tester les accents""",
		    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
		]
		UpperCamelCase     :Union[str, Any]          =	list(zip(self.test_sentences ,  self.test_sentences[::-1]	)	)





	def 						_A (		self				:					Optional[int]	):
		for tokenizer, tf_tokenizer in zip(self.tokenizers ,  self.tf_tokenizers	):
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tokenizer(__lowerCamelCase ,  return_tensors="""tf""" ,  padding="""longest"""	)
				UpperCamelCase     :str          =	tf_tokenizer(__lowerCamelCase	)

				for key in python_outputs.keys():
					self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape	)	)
					self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,  tf.intaa	) == tf_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Dict	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :str          =	tf_tokenizer(self.paired_sentences	)
			UpperCamelCase     :Any          =	tf_tokenizer(
			    text=[sentence[0] for sentence in self.paired_sentences] ,  text_pair=[sentence[1] for sentence in self.paired_sentences] ,  )
			for key in merged_outputs.keys():
				self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,  tf.intaa	) == separated_outputs[key]	)	)





	@slow
	def 						_A (		self				:					List[str]	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[Any]          =	tf.function(__lowerCamelCase	)
			for test_inputs in (self.test_sentences, self.paired_sentences):
				UpperCamelCase     :Any          =	tf.constant(__lowerCamelCase	)
				UpperCamelCase     :List[str]          =	compiled_tokenizer(__lowerCamelCase	)
				UpperCamelCase     :Optional[Any]          =	tf_tokenizer(__lowerCamelCase	)

				for key in eager_outputs.keys():
					self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]	)	)





	@slow
	def 						_A (		self				:					Tuple	):
		for tf_tokenizer in self.tf_tokenizers:
			UpperCamelCase     :List[str]          =	ModelToSave(tokenizer=__lowerCamelCase	)
			UpperCamelCase     :Union[str, Any]          =	tf.convert_to_tensor(self.test_sentences	)
			UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)  # Build model with some sample inputs
			with TemporaryDirectory() as tempdir:
				UpperCamelCase     :List[str]          =	Path(__lowerCamelCase	) / """saved.model"""
				model.save(__lowerCamelCase	)
				UpperCamelCase     :List[Any]          =	tf.keras.models.load_model(__lowerCamelCase	)
			UpperCamelCase     :Dict          =	loaded_model(__lowerCamelCase	)
			# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
			self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output	)	) ,  1E-5	)
