

"""simple docstring"""





import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class 							SCREAMING_SNAKE_CASE   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			PhobertTokenizer
					lowercase__												=			False







					def 			__lowerCAmelCase					(			self     :							str						):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						lowerCAmelCase__      :		Optional[int]    							=      ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
						lowerCAmelCase__      :		Union[str, Any]    							=      dict(zip(lowercase_  ,range(len(lowercase_						)						)						)						)
						lowerCAmelCase__      :		Optional[Any]    							=      ['''#version: 0.2''', '''l à</w>''']
						lowerCAmelCase__      :		List[str]    							=      {'''unk_token''': '''<unk>'''}

						lowerCAmelCase__      :		Any    							=      os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['''vocab_file''']						)
						lowerCAmelCase__      :		Optional[Any]    							=      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(lowercase_						)						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,**lowercase_     :							str						):
						kwargs.update(self.special_tokens_map						)
						return PhobertTokenizer.from_pretrained(self.tmpdirname  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      '''Tôi là VinAI Research'''
						lowerCAmelCase__      :		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     :							Any						):
						lowerCAmelCase__      :		str    							=      PhobertTokenizer(self.vocab_file  ,self.merges_file  ,**self.special_tokens_map						)
						lowerCAmelCase__      :		List[Any]    							=      '''Tôi là VinAI Research'''
						lowerCAmelCase__      :		List[str]    							=      '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
						lowerCAmelCase__      :		List[str]    							=      tokenizer.tokenize(lowercase_						)
						print(lowercase_						)
						self.assertListEqual(lowercase_  ,lowercase_						)

						lowerCAmelCase__      :		int    							=      tokens + [tokenizer.unk_token]

						lowerCAmelCase__      :		Optional[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(lowercase_						)  ,lowercase_						)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




