






"""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 					snake_case		(							__UpperCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	List[Any]						 = MvpTokenizer
   A_						:	List[str]						 = MvpTokenizerFast
   A_						:	str						 = True
   A_						:	Optional[int]						 = filter_roberta_detectors


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

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

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       **_lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     kwargs.update(self.special_tokens_map  )
     return self.tokenizer_class.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       **_lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     kwargs.update(self.special_tokens_map  )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     return "lower newer", "lower newer"


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
     __A									= [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]:
       __A									= tokenizer(_lowerCamelCase,       max_length=len(_lowerCamelCase  ),       padding=_lowerCamelCase,       return_tensors='''pt'''  )
       self.assertIsInstance(_lowerCamelCase,       _lowerCamelCase  )

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


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

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


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

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


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __A									= tokenizer(
           ['''I am a small frog''' * 10_24, '''I am a small frog'''],       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
       self.assertIsInstance(_lowerCamelCase,       _lowerCamelCase  )
       self.assertEqual(batch.input_ids.shape,       (2, 10_24)  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= ["""A long paragraph for summarization."""]
     __A									= [
         """Summary of the text.""",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __A									= tokenizer(_lowerCamelCase,       text_target=_lowerCamelCase,       return_tensors='''pt'''  )
       __A									= inputs["""input_ids"""]
       __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     pass






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

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

         # 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''']  ),       )

         __A									= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
         __A									= 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(
             _lowerCamelCase,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
         self.assertSequenceEqual(
             _lowerCamelCase,       ['''<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							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      **lowercase       ,      ):
				super().__init__(
				    lowercase       ,      split=lowercase       ,      features=lowercase       ,      cache_dir=lowercase       ,      keep_in_memory=lowercase       ,      streaming=lowercase       ,      num_proc=lowercase       ,      **lowercase       ,      )
				A_	:						Any	=     field
				A_	:						Optional[int]	=     path_or_paths if isinstance(lowercase       ,      lowercase							) else {self.split: path_or_paths}
				A_	:						str	=     Json(
				    cache_dir=lowercase       ,      data_files=lowercase       ,      features=lowercase       ,      field=lowercase       ,      **lowercase       ,      )


	def     _a	(self							):
				# Build iterable dataset
				if self.streaming:
							A_	:						Optional[int]	=     self.builder.as_streaming_dataset(split=self.split							)
				# Build regular (map-style) dataset
				else:
							A_	:						Optional[Any]	=     None
							A_	:						Optional[Any]	=     None
							A_	:						Tuple	=     None
							A_	:						Optional[int]	=     None

							self.builder.download_and_prepare(
							    download_config=lowercase       ,      download_mode=lowercase       ,      verification_mode=lowercase       ,      base_path=lowercase       ,      num_proc=self.num_proc       ,      )
							A_	:						Union[str, Any]	=     self.builder.as_dataset(
							    split=self.split       ,      verification_mode=lowercase       ,      in_memory=self.keep_in_memory							)
				return dataset







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase       ,      lowercase = None       ,      lowercase = None       ,      **lowercase       ,      ):
				if num_proc is not None and num_proc <= 0:
							raise ValueError(F'num_proc {num_proc} must be an integer > 0.'							)

				A_	:						Union[str, Any]	=     dataset
				A_	:						Optional[int]	=     path_or_buf
				A_	:						str	=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				A_	:						List[str]	=     num_proc
				A_	:						Union[str, Any]	=     """utf-8"""
				A_	:						Dict	=     to_json_kwargs


	def     _a	(self							):
				A_	:						Optional[Any]	=     self.to_json_kwargs.pop("""path_or_buf"""       ,      lowercase							)
				A_	:						Tuple	=     self.to_json_kwargs.pop("""orient"""       ,      """records"""							)
				A_	:						Union[str, Any]	=     self.to_json_kwargs.pop("""lines"""       ,      True if orient == """records""" else False							)
				A_	:						str	=     self.to_json_kwargs.pop("""index"""       ,      False if orient in ["""split""", """table"""] else True							)
				A_	:						Dict	=     self.to_json_kwargs.pop("""compression"""       ,      lowercase							)

				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=lowercase							) as buffer:
										A_	:						List[str]	=     self._write(file_obj=lowercase       ,      orient=lowercase       ,      lines=lowercase       ,      index=lowercase       ,      **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."""							)
							A_	:						List[Any]	=     self._write(
							    file_obj=self.path_or_buf       ,      orient=lowercase       ,      lines=lowercase       ,      index=lowercase       ,      **self.to_json_kwargs							)
				return written


	def     _a	(self       ,      lowercase							):
				A_,    A_,    A_,    A_,    A_	:						Dict	=     args

				A_	:						Any	=     query_table(
				    table=self.dataset.data       ,      key=slice(lowercase       ,      offset + self.batch_size							)       ,      indices=self.dataset._indices       ,      )
				A_	:						Union[str, Any]	=     batch.to_pandas().to_json(
				    path_or_buf=lowercase       ,      orient=lowercase       ,      lines=lowercase       ,      index=lowercase       ,      **lowercase							)
				if not json_str.endswith("""\n"""							):
							json_str += "\n"
				return json_str.encode(self.encoding							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      **lowercase       ,      ):
				A_	:						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"""       ,      ):
										A_	:						Any	=     self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
										written += file_obj.write(lowercase							)
				else:
							A_,    A_	:						List[Any]	=     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       ,      lowercase       ,      lowercase							)]       ,      )       ,      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(lowercase							)

				return written