




import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				PhobertTokenizer
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->List[Any]:





           '''simple docstring'''

           super().setUp()

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

           A__							=   os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES['''vocab_file'''])
           A__							=   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(UpperCAmelCase__))


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			**UpperCAmelCase__				:     List[Any])						->Tuple:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[Any])						->Dict:





           '''simple docstring'''

           A__							=   '''Tôi là VinAI Research'''
           A__							=   '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
           return input_text, output_text





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->str:





           '''simple docstring'''

           A__							=   PhobertTokenizer(self.vocab_file ,			self.merges_file ,			**self.special_tokens_map)
           A__							=   '''Tôi là VinAI Research'''
           A__							=   '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
           A__							=   tokenizer.tokenize(UpperCAmelCase__)
           print(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   tokens + [tokenizer.unk_token]

           A__							=   [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(UpperCAmelCase__) ,			UpperCAmelCase__)








# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de


