
import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_											=	get_tests_dir("""fixtures/spiece.model""")





@require_sentencepiece
@require_tokenizers
class   __UpperCamelCase				(    lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	ErnieMTokenizer
     lowerCAmelCase_     =	False
     lowerCAmelCase_     =	True
     lowerCAmelCase_     =	False
     lowerCAmelCase_     =	False



     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       super().setUp()

       # We have a SentencePiece fixture for testing
       __SCREAMING_SNAKE_CASE     :					Any			=						ErnieMTokenizer(_A      ,		unk_token='''<unk>'''      ,		pad_token='''<pad>'''				)
       tokenizer.save_pretrained(self.tmpdirname				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						'''this is a test'''
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						'''this is a test'''
       return input_text, output_text



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						'''<pad>'''
       __SCREAMING_SNAKE_CASE     :					int			=						0

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



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]      ,		'''<pad>'''				)
       self.assertEqual(vocab_keys[1]      ,		'''<unk>'''				)
       self.assertEqual(vocab_keys[-1]      ,		'''▁eloquent'''				)
       self.assertEqual(len(_A				)      ,		3_0000				)



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       self.assertEqual(self.get_tokenizer().vocab_size      ,		3_0000				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       if not self.test_rust_tokenizer:
         return

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.get_tokenizer()
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.get_rust_tokenizer()

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						'''I was born in 92000, and this is falsé.'''

       __SCREAMING_SNAKE_CASE     :					Tuple			=						tokenizer.tokenize(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						rust_tokenizer.tokenize(_A				)
       self.assertListEqual(_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					Tuple			=						tokenizer.encode(_A      ,		add_special_tokens=_A				)
       __SCREAMING_SNAKE_CASE     :					int			=						rust_tokenizer.encode(_A      ,		add_special_tokens=_A				)
       self.assertListEqual(_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.get_rust_tokenizer()
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						tokenizer.encode(_A				)
       __SCREAMING_SNAKE_CASE     :					int			=						rust_tokenizer.encode(_A				)
       self.assertListEqual(_A      ,		_A				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						ErnieMTokenizer(_A      ,		do_lower_case=_A      ,		unk_token='''<unk>'''      ,		pad_token='''<pad>'''				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						tokenizer.tokenize('''This is a test'''				)
       self.assertListEqual(_A      ,		['''▁this''', '''▁is''', '''▁a''', '''▁test''']				)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(_A				)      ,		[48, 25, 21, 1289]				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           _A      ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						tokenizer.convert_tokens_to_ids(_A				)
       self.assertListEqual(_A      ,		[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]				)

       __SCREAMING_SNAKE_CASE     :					int			=						tokenizer.convert_ids_to_tokens(_A				)
       self.assertListEqual(
           _A      ,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						ErnieMTokenizer(_A      ,		unk_token='''<unk>'''      ,		pad_token='''<pad>'''				)

       __SCREAMING_SNAKE_CASE     :					int			=						tokenizer.encode('''sequence builders'''				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						tokenizer.encode('''multi-sequence build'''				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						tokenizer.build_inputs_with_special_tokens(_A				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						tokenizer.build_inputs_with_special_tokens(_A      ,		_A				)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]




     @slow
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						{'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 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, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 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, 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]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_A      ,		model_name='''susnato/ernie-m-base_pytorch'''      ,		sequences=[
               '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
               '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
               '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
               '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
               '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
               '''conditioning on both left and right context in all layers.''',
               '''The quick brown fox jumps over the lazy dog.''',
           ]      ,		)


import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						load_tool('''translation'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						load_tool('''translation'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.remote_tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						self.tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.remote_tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.tool(*_A				)

       # There is a single output
       if len(self.tool.outputs				) == 1:
         __SCREAMING_SNAKE_CASE     :					Any			=						[outputs]

       self.assertListEqual(output_types(_A				)      ,		self.tool.outputs				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.tool(*_A				)

       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						[outputs]

       self.assertEqual(len(_A				)      ,		len(self.tool.outputs				)				)

       for output, output_type in zip(_A      ,		self.tool.outputs				):
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						AGENT_TYPE_MAPPING[output_type]
         self.assertTrue(isinstance(_A      ,		_A				)				)




     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[]

       for _input, input_type in zip(_A      ,		self.tool.inputs				):
         if isinstance(_A      ,		_A				):
           _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
         else:
           _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.tool(*_A				)

       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Dict			=						[outputs]

       self.assertEqual(len(_A				)      ,		len(self.tool.outputs				)				)
