




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 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						BarthezTokenizer
     _a							 =						BarthezTokenizerFast
     _a							 =						True
     _a							 =						True

     def 					a__	(  self				)						->							Tuple:
           super().setUp()

           _A							:     List[str]			=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
           tokenizer.save_pretrained(self.tmpdirname				)
           tokenizer.save_pretrained(self.tmpdirname		,     legacy_format=_a				)
           _A							:     List[str]			=						tokenizer

     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						"""<pad>"""
           _A							:     List[Any]			=						1

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

     def 					a__	(  self				)						->							str:
           _A							:     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(_a				)		,     10_1122				)

     def 					a__	(  self				)						->							List[Any]:
           self.assertEqual(self.get_tokenizer().vocab_size		,     10_1122				)

     @require_torch
     def 					a__	(  self				)						->							Tuple:
           _A							:     List[str]			=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           _A							:     Optional[int]			=						[0, 57, 3018, 7_0307, 91, 2]

           _A							:     List[str]			=						self.tokenizer(
               _a		,     max_length=len(_a				)		,     padding=_a		,     truncation=_a		,     return_tensors="""pt"""				)
           self.assertIsInstance(_a		,     _a				)

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

     def 					a__	(  self				)						->							List[str]:
           if not self.test_rust_tokenizer:
                 return

           _A							:     Union[str, Any]			=						self.get_tokenizer()
           _A							:     Tuple			=						self.get_rust_tokenizer()

           _A							:     Optional[int]			=						"""I was born in 92000, and this is falsé."""

           _A							:     Any			=						tokenizer.tokenize(_a				)
           _A							:     Union[str, Any]			=						rust_tokenizer.tokenize(_a				)
           self.assertListEqual(_a		,     _a				)

           _A							:     Any			=						tokenizer.encode(_a		,     add_special_tokens=_a				)
           _A							:     List[str]			=						rust_tokenizer.encode(_a		,     add_special_tokens=_a				)
           self.assertListEqual(_a		,     _a				)

           _A							:     Optional[Any]			=						self.get_rust_tokenizer()
           _A							:     Any			=						tokenizer.encode(_a				)
           _A							:     List[str]			=						rust_tokenizer.encode(_a				)
           self.assertListEqual(_a		,     _a				)



     @slow
     def 					a__	(  self				)						->							int:
           # fmt: off
           _A							:     int			=						{"""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.
           _A							:     Any			=						[
               """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=_a		,     model_name="""moussaKam/mbarthez"""		,     revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""		,     sequences=_a		,     )



# 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

snake_case :		List[str]							      =  "facebook/wmt19-en-de"
snake_case :		Dict							      =  FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case :		List[str]							      =  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,
    )
)

snake_case :		int							      =  FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case :		Optional[Any]							      =  tokenizer(["Making tiny model"], return_tensors="pt")
snake_case :		List[str]							      =  tiny_model(**batch)

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

# Save
snake_case :		Dict							      =  "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
