

'''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      lowerCAmelCase   (					__a      ,	unittest.TestCase      ):
       lowerCAmelCase_				=       MvpTokenizer
       lowerCAmelCase_				=       MvpTokenizerFast
       lowerCAmelCase_				=       True
       lowerCAmelCase_				=       filter_roberta_detectors







       def 	snake_case      (							self	:       Optional[int]					):







            """simple docstring"""







            super().setUp()
            __lowercase							=[
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''\u0120''',
                '''\u0120l''',
                '''\u0120n''',
                '''\u0120lo''',
                '''\u0120low''',
                '''er''',
                '''\u0120lowest''',
                '''\u0120newer''',
                '''\u0120wider''',
                '''<unk>''',
            ]
            __lowercase							=dict(zip(_A  ,					range(len(_A					)					)					)					)
            __lowercase							=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
            __lowercase							={'''unk_token''': '''<unk>'''}

            __lowercase							=os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']					)
            __lowercase							=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(_A					) + '\n'					)
            with open(self.merges_file  ,					'w'  ,					encoding='utf-8'					) as fp:
                 fp.write('\n'.join(_A					)					)







       def 	snake_case      (							self	:       Tuple  ,					**__lowercase	:       Any					):







            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname  ,					**_A					)







       def 	snake_case      (							self	:       int  ,					**__lowercase	:       Union[str, Any]					):







            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,					**_A					)







       def 	snake_case      (							self	:       Optional[int]  ,					__lowercase	:       List[Any]					):







            """simple docstring"""







            return "lower newer", "lower newer"







       @cached_property
       def 	snake_case      (							self	:       Optional[int]					):







            """simple docstring"""







            return MvpTokenizer.from_pretrained('RUCAIBox/mvp'					)







       @cached_property
       def 	snake_case      (							self	:       Optional[Any]					):







            """simple docstring"""







            return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'					)







       @require_torch
       def 	snake_case      (							self	:       Dict					):







            """simple docstring"""







            __lowercase							=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __lowercase							=[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __lowercase							=tokenizer(_A  ,					max_length=len(_A					)  ,					padding=_A  ,					return_tensors='pt'					)
                 self.assertIsInstance(_A  ,					_A					)

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







       @require_torch
       def 	snake_case      (							self	:       Dict					):







            """simple docstring"""







            __lowercase							=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __lowercase							=tokenizer(_A  ,					padding=_A  ,					return_tensors='pt'					)
                 # check if input_ids are returned and no labels
                 self.assertIn('input_ids'  ,					_A					)
                 self.assertIn('attention_mask'  ,					_A					)
                 self.assertNotIn('labels'  ,					_A					)
                 self.assertNotIn('decoder_attention_mask'  ,					_A					)







       @require_torch
       def 	snake_case      (							self	:       List[str]					):







            """simple docstring"""







            __lowercase							=[
                '''Summary of the text.''',
                '''Another summary.''',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __lowercase							=tokenizer(text_target=_A  ,					max_length=32  ,					padding='max_length'  ,					return_tensors='pt'					)
                 self.assertEqual(32  ,					targets['input_ids'].shape[1]					)







       @require_torch
       def 	snake_case      (							self	:       int					):







            """simple docstring"""







            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __lowercase							=tokenizer(
                     ['I am a small frog' * 1024, 'I am a small frog']  ,					padding=_A  ,					truncation=_A  ,					return_tensors='pt'					)
                 self.assertIsInstance(_A  ,					_A					)
                 self.assertEqual(batch.input_ids.shape  ,					(2, 1024)					)







       @require_torch
       def 	snake_case      (							self	:       Union[str, Any]					):







            """simple docstring"""







            __lowercase							=['''A long paragraph for summarization.''']
            __lowercase							=[
                '''Summary of the text.''',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __lowercase							=tokenizer(_A  ,					text_target=_A  ,					return_tensors='pt'					)
                 __lowercase							=inputs['''input_ids''']
                 __lowercase							=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 	snake_case      (							self	:       Any					):







            """simple docstring"""







            pass







       def 	snake_case      (							self	:       List[str]					):







            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                      __lowercase							=self.rust_tokenizer_class.from_pretrained(_A  ,					**_A					)
                      __lowercase							=self.tokenizer_class.from_pretrained(_A  ,					**_A					)
                      __lowercase							='''A, <mask> AllenNLP sentence.'''
                      __lowercase							=tokenizer_r.encode_plus(_A  ,					add_special_tokens=_A  ,					return_token_type_ids=_A					)
                      __lowercase							=tokenizer_p.encode_plus(_A  ,					add_special_tokens=_A  ,					return_token_type_ids=_A					)

                      # 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']					)  ,					)

                      __lowercase							=tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
                      __lowercase							=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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                      self.assertSequenceEqual(tokens_r['input_ids']  ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

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






'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[str]						,						_A				:    NestedDataStructureLike[PathLike]						,						_A				:    Optional[NamedSplit] = None						,						_A				:    Optional[Features] = None						,						_A				:    str = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    Optional[str] = None						,						_A				:    Optional[int] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          super().__init__(
              _A						,						split=_A						,						features=_A						,						cache_dir=_A						,						keep_in_memory=_A						,						streaming=_A						,						num_proc=_A						,						**_A						,						)
          UpperCAmelCase__			:		List[str]													=				field
          UpperCAmelCase__			:		Optional[Any]													=				path_or_paths if isinstance(_A						,						_A ) else {self.split: path_or_paths}
          UpperCAmelCase__			:		Any													=				Json(
              cache_dir=_A						,						data_files=_A						,						features=_A						,						field=_A						,						**_A						,						)






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if self.streaming:
              UpperCAmelCase__			:		int													=				self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__			:		List[str]													=				None
              UpperCAmelCase__			:		Dict													=				None
              UpperCAmelCase__			:		Optional[int]													=				None
              UpperCAmelCase__			:		List[Any]													=				None

              self.builder.download_and_prepare(
                  download_config=_A						,						download_mode=_A						,						verification_mode=_A						,						base_path=_A						,						num_proc=self.num_proc						,						)
              UpperCAmelCase__			:		str													=				self.builder.as_dataset(
                  split=self.split						,						verification_mode=_A						,						in_memory=self.keep_in_memory )
          return dataset




class 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Dataset						,						_A				:    Union[PathLike, BinaryIO]						,						_A				:    Optional[int] = None						,						_A				:    Optional[int] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
              raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

          UpperCAmelCase__			:		Dict													=				dataset
          UpperCAmelCase__			:		Any													=				path_or_buf
          UpperCAmelCase__			:		List[str]													=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__			:		Tuple													=				num_proc
          UpperCAmelCase__			:		Any													=				'''utf-8'''
          UpperCAmelCase__			:		Optional[int]													=				to_json_kwargs






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.to_json_kwargs.pop('''path_or_buf'''						,						_A )
          UpperCAmelCase__			:		Optional[int]													=				self.to_json_kwargs.pop('''orient'''						,						'''records''' )
          UpperCAmelCase__			:		Tuple													=				self.to_json_kwargs.pop('''lines'''						,						True if orient == '''records''' else False )
          UpperCAmelCase__			:		str													=				self.to_json_kwargs.pop('''index'''						,						False if orient in ['''split''', '''table'''] else True )
          UpperCAmelCase__			:		Optional[Any]													=				self.to_json_kwargs.pop('''compression'''						,						_A )

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(f"""`datasets` currently does not support {compression} compression""" )

          if isinstance(self.path_or_buf						,						(str, bytes, os.PathLike) ):
              with fsspec.open(self.path_or_buf						,						'''wb'''						,						compression=_A ) as buffer:
                  UpperCAmelCase__			:		Union[str, Any]													=				self._write(file_obj=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          else:
              if compression:
                  raise NotImplementedError(
                      f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                      ''' was passed. Please provide a local path instead.''' )
              UpperCAmelCase__			:		Union[str, Any]													=				self._write(
                  file_obj=self.path_or_buf						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          return written






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				args

          UpperCAmelCase__			:		Dict													=				query_table(
              table=self.dataset.data						,						key=slice(_A						,						offset + self.batch_size )						,						indices=self.dataset._indices						,						)
          UpperCAmelCase__			:		str													=				batch.to_pandas().to_json(
              path_or_buf=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**_A )
          if not json_str.endswith('''\n''' ):
              json_str += "\n"
          return json_str.encode(self.encoding )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    BinaryIO						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    Any						,						**_A				:    Any						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				0

          if self.num_proc is None or self.num_proc == 1:
              for offset in logging.tqdm(
                  range(0						,						len(self.dataset )						,						self.batch_size )						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                  UpperCAmelCase__			:		Optional[int]													=				self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                  written += file_obj.write(_A )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				len(self.dataset ), self.batch_size
              with multiprocessing.Pool(self.num_proc ) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json						,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,						_A						,						_A )]						,						)						,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                      written += file_obj.write(_A )

          return written
