


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase_           =	'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'


class        A_					(       unittest.TestCase					,      __UpperCamelCase    ):




      '''simple docstring'''


      def      _snake_case	(				self:       Optional[int]				):
       __lowerCamelCase		:							Optional[Any]							=						load_tool('summarization'				)
       self.tool.setup()
       __lowerCamelCase		:							List[Any]							=						load_tool('summarization'						,      remote=a				)




      def      _snake_case	(				self:       int				):
       __lowerCamelCase		:							List[str]							=						self.tool(a				)
       self.assertEqual(
           a						,      'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'						,      )




      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							List[Any]							=						self.remote_tool(a				)
       self.assertEqual(
           a						,      'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'						,      )




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							Any							=						self.tool(text=a				)
       self.assertEqual(
           a						,      'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'						,      )




      def      _snake_case	(				self:       Optional[int]				):
       __lowerCamelCase		:							List[Any]							=						self.remote_tool(text=a				)
       self.assertEqual(
           a						,      'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'						,      )





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        A_					(       __UpperCamelCase					,      unittest.TestCase    ):




      '''simple docstring'''


      __snake_case					       =   ErnieMTokenizer
      __snake_case					       =   False
      __snake_case					       =   True
      __snake_case					       =   False
      __snake_case					       =   False




      def      _snake_case	(				self:       Optional[int]				):
       super().setUp()

       # We have a SentencePiece fixture for testing
       __lowerCamelCase		:							List[str]							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)
       tokenizer.save_pretrained(self.tmpdirname				)




      def      _snake_case	(				self:       Dict						,      a:       List[Any]				):
       __lowerCamelCase		:							Optional[Any]							=						'this is a test'
       __lowerCamelCase		:							Optional[int]							=						'this is a test'
       return input_text, output_text




      def      _snake_case	(				self:       Tuple				):
       __lowerCamelCase		:							List[str]							=						'<pad>'
       __lowerCamelCase		:							Union[str, Any]							=						0

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




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							List[str]							=						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      _snake_case	(				self:       Tuple				):
       self.assertEqual(self.get_tokenizer().vocab_size						,      3_0000				)




      def      _snake_case	(				self:       int				):
       if not self.test_rust_tokenizer:
        return

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

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

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.tokenize(a				)
       __lowerCamelCase		:							Dict							=						rust_tokenizer.tokenize(a				)
       self.assertListEqual(a						,      a				)

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

       __lowerCamelCase		:							str							=						self.get_rust_tokenizer()
       __lowerCamelCase		:							str							=						tokenizer.encode(a				)
       __lowerCamelCase		:							List[str]							=						rust_tokenizer.encode(a				)
       self.assertListEqual(a						,      a				)




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							Union[str, Any]							=						ErnieMTokenizer(a						,      do_lower_case=a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

       __lowerCamelCase		:							List[str]							=						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]				)

       __lowerCamelCase		:							Optional[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', '.']				)
       __lowerCamelCase		:							Any							=						tokenizer.convert_tokens_to_ids(a				)
       self.assertListEqual(a						,      [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]				)

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




      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							Tuple							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

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

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.build_inputs_with_special_tokens(a				)
       __lowerCamelCase		:							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      _snake_case	(				self:       List[Any]				):
       # fmt: off
       __lowerCamelCase		:							int							=						{'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.',
           ]						,      )

