





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


UpperCamelCase__          =get_tests_dir('fixtures/spiece.model')





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






       '''simple docstring'''


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




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            super().setUp()

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




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							"this is a test"
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							"this is a test"
            return input_text, output_text




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"<pad>"
            _SCREAMING_SNAKE_CASE				:		Any  			=							0

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




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		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(__lowerCamelCase							)			,  3_0_0_0_0							)




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            self.assertEqual(self.get_tokenizer().vocab_size			,  3_0_0_0_0							)




       def 			UpperCamelCase_	(					self							)	->   int:
            if not self.test_rust_tokenizer:
                 return

            _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.get_tokenizer()
            _SCREAMING_SNAKE_CASE				:		str  			=							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(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							rust_tokenizer.tokenize(__lowerCamelCase							)
            self.assertListEqual(__lowerCamelCase			,  __lowerCamelCase							)

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

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




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		int  			=							ErnieMTokenizer(__lowerCamelCase			,  do_lower_case=__lowerCamelCase			,  unk_token="<unk>"			,  pad_token="<pad>"							)

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							tokenizer.tokenize("This is a test"							)
            self.assertListEqual(__lowerCamelCase			,  ["▁this", "▁is", "▁a", "▁test"]							)

            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							)			,  [4_8, 2_5, 2_1, 1_2_8_9]							)

            _SCREAMING_SNAKE_CASE				:		str  			=							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(
                __lowerCamelCase			,  ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]							)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							tokenizer.convert_tokens_to_ids(__lowerCamelCase							)
            self.assertListEqual(__lowerCamelCase			,  [3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]							)

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




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		int  			=							ErnieMTokenizer(__lowerCamelCase			,  unk_token="<unk>"			,  pad_token="<pad>"							)

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							tokenizer.encode("sequence builders"							)
            _SCREAMING_SNAKE_CASE				:		int  			=							tokenizer.encode("multi-sequence build"							)

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		str  			=							tokenizer.build_inputs_with_special_tokens(__lowerCamelCase			,  __lowerCamelCase							)

            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							)	->   Union[str, Any]:
            # fmt: off
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							{"input_ids": [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=__lowerCamelCase			,  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.",
                ]			,  )

'''simple docstring'''



import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil
@pytest.fixture
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = tmp_path / """text.txt"""
   _SCREAMING_SNAKE_CASE          = textwrap.dedent(
       """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """  )
   with open(SCREAMING_SNAKE_CASE_			,					"""w"""			,					encoding="""utf-8"""  ) as f:
      f.write(SCREAMING_SNAKE_CASE_  )
   return str(SCREAMING_SNAKE_CASE_  )
@pytest.fixture
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = tmp_path / """text_with_image.txt"""
   with open(SCREAMING_SNAKE_CASE_			,					"""w"""			,					encoding="""utf-8"""  ) as f:
      f.write(SCREAMING_SNAKE_CASE_  )
   return str(SCREAMING_SNAKE_CASE_  )
@pytest.mark.parametrize("""keep_linebreaks"""			,					[True, False]  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   with open(SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""  ) as f:
      _SCREAMING_SNAKE_CASE          = f.read().splitlines(keepends=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = Text(keep_linebreaks=SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""  )
   _SCREAMING_SNAKE_CASE          = text._generate_tables([[text_file]]  )
   _SCREAMING_SNAKE_CASE          = pa.concat_tables([table for _, table in generator]  ).to_pydict()["""text"""]
   assert generated_content == expected_content
@require_pil
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   with open(SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""  ) as f:
      _SCREAMING_SNAKE_CASE          = f.read().splitlines()[0]
   _SCREAMING_SNAKE_CASE          = Text(encoding="""utf-8"""			,					features=Features({"""image""": Image()}  )  )
   _SCREAMING_SNAKE_CASE          = text._generate_tables([[text_file_with_image]]  )
   _SCREAMING_SNAKE_CASE          = pa.concat_tables([table for _, table in generator]  )
   assert pa_table.schema.field("""image"""  ).type == Image()()
   _SCREAMING_SNAKE_CASE          = pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize("""sample_by"""			,					["""line""", """paragraph""", """document"""]  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   List[Any]:




   """simple docstring"""
   with open(SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""  ) as f:
      _SCREAMING_SNAKE_CASE          = f.read()
   if sample_by == "line":
      _SCREAMING_SNAKE_CASE          = expected_content.splitlines()
   elif sample_by == "paragraph":
      _SCREAMING_SNAKE_CASE          = expected_content.split("""\n\n"""  )
   elif sample_by == "document":
      _SCREAMING_SNAKE_CASE          = [expected_content]
   _SCREAMING_SNAKE_CASE          = Text(sample_by=SCREAMING_SNAKE_CASE_			,					encoding="""utf-8"""			,					chunksize=1_00  )
   _SCREAMING_SNAKE_CASE          = text._generate_tables([[text_file]]  )
   _SCREAMING_SNAKE_CASE          = pa.concat_tables([table for _, table in generator]  ).to_pydict()["""text"""]
   assert generated_content == expected_content






