





'''simple docstring'''






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  MvpTokenizer
      _lowercase:     Optional[int]			=  MvpTokenizerFast
      _lowercase:     List[Any]			=  True
      _lowercase:     Tuple			=  filter_roberta_detectors




      def 							lowercase__       (     self     :					str    )							-> Dict:
            super().setUp()
            _lowerCAmelCase					=				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
            ]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """<unk>"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(__snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(__snake_case    )    )




      def 							lowercase__       (     self     :					Tuple     ,     **__snake_case     :					Any    )							-> Optional[Any]:
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					List[Any]    )							-> List[str]:
            kwargs.update(self.special_tokens_map    )
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					str    )							-> List[str]:
            return "lower newer", "lower newer"




      @cached_property
      def 							lowercase__       (     self     :					int    )							-> int:
            return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""    )




      @cached_property
      def 							lowercase__       (     self     :					Any    )							-> Any:
            return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""    )




      @require_torch
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Any:
            _lowerCAmelCase					=				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            _lowerCAmelCase					=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     max_length=len(__snake_case    )     ,     padding=__snake_case     ,     return_tensors="""pt"""    )
                  self.assertIsInstance(__snake_case     ,     __snake_case    )

                  self.assertEqual((2, 9)     ,     batch.input_ids.shape    )
                  self.assertEqual((2, 9)     ,     batch.attention_mask.shape    )
                  _lowerCAmelCase					=				batch.input_ids.tolist()[0]
                  self.assertListEqual(__snake_case     ,     __snake_case    )
                  # Test that special tokens are reset




      @require_torch
      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case     ,     return_tensors="""pt"""    )
                  # check if input_ids are returned and no labels
                  self.assertIn("""input_ids"""     ,     __snake_case    )
                  self.assertIn("""attention_mask"""     ,     __snake_case    )
                  self.assertNotIn("""labels"""     ,     __snake_case    )
                  self.assertNotIn("""decoder_attention_mask"""     ,     __snake_case    )




      @require_torch
      def 							lowercase__       (     self     :					Optional[Any]    )							-> List[Any]:
            _lowerCAmelCase					=				[
                """Summary of the text.""",
                """Another summary.""",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _lowerCAmelCase					=				tokenizer(text_target=__snake_case     ,     max_length=32     ,     padding="""max_length"""     ,     return_tensors="""pt"""    )
                  self.assertEqual(32     ,     targets["""input_ids"""].shape[1]    )




      @require_torch
      def 							lowercase__       (     self     :					Tuple    )							-> List[Any]:
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _lowerCAmelCase					=				tokenizer(
                      ["""I am a small frog""" * 10_24, """I am a small frog"""]     ,     padding=__snake_case     ,     truncation=__snake_case     ,     return_tensors="""pt"""    )
                  self.assertIsInstance(__snake_case     ,     __snake_case    )
                  self.assertEqual(batch.input_ids.shape     ,     (2, 10_24)    )




      @require_torch
      def 							lowercase__       (     self     :					Any    )							-> int:
            _lowerCAmelCase					=				["""A long paragraph for summarization."""]
            _lowerCAmelCase					=				[
                """Summary of the text.""",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     text_target=__snake_case     ,     return_tensors="""pt"""    )
                  _lowerCAmelCase					=				inputs["""input_ids"""]
                  _lowerCAmelCase					=				inputs["""labels"""]
                  self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
                  self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
                  self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
                  self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )




      def 							lowercase__       (     self     :					List[str]    )							-> Tuple:
            pass




      def 							lowercase__       (     self     :					Dict    )							-> List[str]:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
                        _lowerCAmelCase					=				self.rust_tokenizer_class.from_pretrained(__snake_case     ,     **__snake_case    )
                        _lowerCAmelCase					=				self.tokenizer_class.from_pretrained(__snake_case     ,     **__snake_case    )
                        _lowerCAmelCase					=				"""A, <mask> AllenNLP sentence."""
                        _lowerCAmelCase					=				tokenizer_r.encode_plus(__snake_case     ,     add_special_tokens=__snake_case     ,     return_token_type_ids=__snake_case    )
                        _lowerCAmelCase					=				tokenizer_p.encode_plus(__snake_case     ,     add_special_tokens=__snake_case     ,     return_token_type_ids=__snake_case    )

                        # token_type_ids should put 0 everywhere
                        self.assertEqual(sum(tokens_r["""token_type_ids"""]    )     ,     sum(tokens_p["""token_type_ids"""]    )    )

                        # attention_mask should put 1 everywhere, so sum over length should be 1
                        self.assertEqual(
                            sum(tokens_r["""attention_mask"""]    ) / len(tokens_r["""attention_mask"""]    )     ,     sum(tokens_p["""attention_mask"""]    ) / len(tokens_p["""attention_mask"""]    )     ,     )

                        _lowerCAmelCase					=				tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
                        _lowerCAmelCase					=				tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]    )

                        # Rust correctly handles the space before the mask while python doesnt
                        self.assertSequenceEqual(tokens_p["""input_ids"""]     ,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
                        self.assertSequenceEqual(tokens_r["""input_ids"""]     ,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

                        self.assertSequenceEqual(
                            __snake_case     ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
                        self.assertSequenceEqual(
                            __snake_case     ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )













'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					Union[str, Any]     ,     __snake_case     :					Callable     ,     __snake_case     :					Optional[Features] = None     ,     __snake_case     :					str = None     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional[dict] = None     ,     __snake_case     :					Optional[int] = None     ,     **__snake_case     :					Optional[Any]     ,     )							-> Union[str, Any]:
            super().__init__(
                features=__snake_case     ,     cache_dir=__snake_case     ,     keep_in_memory=__snake_case     ,     streaming=__snake_case     ,     num_proc=__snake_case     ,     **__snake_case     ,     )
            _lowerCAmelCase					=				Generator(
                cache_dir=__snake_case     ,     features=__snake_case     ,     generator=__snake_case     ,     gen_kwargs=__snake_case     ,     **__snake_case     ,     )




      def 							lowercase__       (     self     :					str    )							-> Dict:
            # Build iterable dataset
            if self.streaming:
                  _lowerCAmelCase					=				self.builder.as_streaming_dataset(split="""train"""    )
            # Build regular (map-style) dataset
            else:
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None

                  self.builder.download_and_prepare(
                      download_config=__snake_case     ,     download_mode=__snake_case     ,     verification_mode=__snake_case     ,     base_path=__snake_case     ,     num_proc=self.num_proc     ,     )
                  _lowerCAmelCase					=				self.builder.as_dataset(
                      split="""train"""     ,     verification_mode=__snake_case     ,     in_memory=self.keep_in_memory    )
            return dataset






