




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase     :		Union[str, Any]								=       """▁"""

_lowerCamelCase     :		Dict								=       get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				BertGenerationTokenizer
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				True


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           super().setUp()

           A__							=   BertGenerationTokenizer(UpperCAmelCase__ ,			keep_accents=UpperCAmelCase__)
           tokenizer.save_pretrained(self.tmpdirname)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

           A__							=   '''<s>'''
           A__							=   1

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

           A__							=   list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0] ,			'''<unk>''')
           self.assertEqual(vocab_keys[1] ,			'''<s>''')
           self.assertEqual(vocab_keys[-1] ,			'''<pad>''')
           self.assertEqual(len(UpperCAmelCase__) ,			1_002)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Union[str, Any]:





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size ,			1_000)


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





           '''simple docstring'''

           A__							=   BertGenerationTokenizer(UpperCAmelCase__ ,			keep_accents=UpperCAmelCase__)

           A__							=   tokenizer.tokenize('''This is a test''')
           self.assertListEqual(UpperCAmelCase__ ,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCAmelCase__) ,			[285, 46, 10, 170, 382] ,			)

           A__							=   tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
           self.assertListEqual(
               UpperCAmelCase__ ,			[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''9''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''é''',
                   '''.''',
               ] ,			)
           A__							=   tokenizer.convert_tokens_to_ids(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__ ,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,			)

           A__							=   tokenizer.convert_ids_to_tokens(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__ ,			[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''<unk>''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''<unk>''',
                   '''.''',
               ] ,			)


    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Union[str, Any]:





           '''simple docstring'''

           return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')


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





           '''simple docstring'''

           A__							=   '''Hello World!'''
           A__							=   [18_536, 2_260, 101]

           self.assertListEqual(UpperCAmelCase__ ,			self.big_tokenizer.encode(UpperCAmelCase__))


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->List[Any]:





           '''simple docstring'''

           A__							=   (
               '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
               ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
           )
           A__							=   [
               871,
               419,
               358,
               946,
               991,
               2_521,
               452,
               358,
               1_357,
               387,
               7_751,
               3_536,
               112,
               985,
               456,
               126,
               865,
               938,
               5_400,
               5_734,
               458,
               1_368,
               467,
               786,
               2_462,
               5_246,
               1_159,
               633,
               865,
               4_519,
               457,
               582,
               852,
               2_557,
               427,
               916,
               508,
               405,
               34_324,
               497,
               391,
               408,
               11_342,
               1_244,
               385,
               100,
               938,
               985,
               456,
               574,
               362,
               12_597,
               3_200,
               3_129,
               1_172,
           ]

           self.assertListEqual(UpperCAmelCase__ ,			self.big_tokenizer.encode(UpperCAmelCase__))


    @require_torch
    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Dict:





           '''simple docstring'''

           import torch

           from transformers import BertGenerationConfig, BertGenerationEncoder

           # Build sequence
           A__							=   list(self.big_tokenizer.get_vocab().keys())[:10]
           A__							=   ''' '''.join(UpperCAmelCase__)
           A__							=   self.big_tokenizer.encode_plus(UpperCAmelCase__ ,			return_tensors='''pt''' ,			return_token_type_ids=UpperCAmelCase__)
           A__							=   self.big_tokenizer.batch_encode_plus(
               [sequence + ''' ''' + sequence] ,			return_tensors='''pt''' ,			return_token_type_ids=UpperCAmelCase__)

           A__							=   BertGenerationConfig()
           A__							=   BertGenerationEncoder(UpperCAmelCase__)

           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                  model(**UpperCAmelCase__)
                  model(**UpperCAmelCase__)





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Tuple:





           '''simple docstring'''

           A__							=   {'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 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, 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, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 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, 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, 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]], '''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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0], [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, 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, 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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase__ ,			model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' ,			revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' ,			)








# 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


