


"""simple docstring"""






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  BartTokenizer
							__magic_name__					:     Dict				  =  BartTokenizerFast
							__magic_name__					:     List[Any]				  =  True
							__magic_name__					:     Union[str, Any]				  =  filter_roberta_detectors




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




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

								snake_case__  :						int		=      os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES["vocab_file"])
								snake_case__  :						Tuple		=      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        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	Any:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								return "lower newer", "lower newer"




							@cached_property
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								return BartTokenizer.from_pretrained("facebook/bart-large")




							@cached_property
							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								return BartTokenizerFast.from_pretrained("facebook/bart-large")




							@require_torch
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						str		=      ["A long paragraph for summarization.", "Another paragraph for summarization."]
								snake_case__  :						Dict		=      [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Union[str, Any]		=      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)
									snake_case__  :						List[Any]		=      batch.input_ids.tolist()[0]
									self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)
									# Test that special tokens are reset




							@require_torch
							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						str		=      ["A long paragraph for summarization.", "Another paragraph for summarization."]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Optional[Any]		=      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        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Any		=      [
								    "Summary of the text.",
								    "Another summary.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Dict		=      tokenizer(text_target=lowerCamelCase__	,					max_length=32	,					padding="max_length"	,					return_tensors="pt")
									self.assertEqual(32	,					targets["input_ids"].shape[1])




							@require_torch
							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Optional[Any]		=      tokenizer(
									    ["I am a small frog" * 1_024, "I am a small frog"]	,					padding=lowerCamelCase__	,					truncation=lowerCamelCase__	,					return_tensors="pt")
									self.assertIsInstance(lowerCamelCase__	,					lowerCamelCase__)
									self.assertEqual(batch.input_ids.shape	,					(2, 1_024))




							@require_torch
							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      ["A long paragraph for summarization."]
								snake_case__  :						Optional[Any]		=      [
								    "Summary of the text.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Optional[int]		=      tokenizer(lowerCamelCase__	,					return_tensors="pt")
									snake_case__  :						List[Any]		=      tokenizer(text_target=lowerCamelCase__	,					return_tensors="pt")
									snake_case__  :						Any		=      inputs["input_ids"]
									snake_case__  :						Dict		=      targets["input_ids"]
									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        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								pass







							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										snake_case__  :						Optional[Any]		=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase__	,					**lowerCamelCase__)
										snake_case__  :						Tuple		=      self.tokenizer_class.from_pretrained(lowerCamelCase__	,					**lowerCamelCase__)
										snake_case__  :						List[str]		=      "A, <mask> AllenNLP sentence."
										snake_case__  :						int		=      tokenizer_r.encode_plus(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__	,					return_token_type_ids=lowerCamelCase__)
										snake_case__  :						Tuple		=      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"])	,					)

										snake_case__  :						Tuple		=      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
										snake_case__  :						Optional[int]		=      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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
										self.assertSequenceEqual(tokens_r["input_ids"]	,					[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

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







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

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

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            __UpperCamelCase  ,						split=__UpperCamelCase  ,						features=__UpperCamelCase  ,						cache_dir=__UpperCamelCase  ,						keep_in_memory=__UpperCamelCase  ,						streaming=__UpperCamelCase  ,						num_proc=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							path_or_paths if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else {self.split: path_or_paths}
        UpperCamelCase_											=							Csv(
            cache_dir=__UpperCamelCase  ,						data_files=__UpperCamelCase  ,						features=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        if self.streaming:
               UpperCamelCase_											=							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None

               self.builder.download_and_prepare(
                   download_config=__UpperCamelCase  ,						download_mode=__UpperCamelCase  ,						verification_mode=__UpperCamelCase  ,						base_path=__UpperCamelCase  ,						num_proc=self.num_proc  ,						)
               UpperCamelCase_											=							self.builder.as_dataset(
                   split=self.split  ,						verification_mode=__UpperCamelCase  ,						in_memory=self.keep_in_memory  )
        return dataset



class 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """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_											=							dataset
        UpperCamelCase_											=							path_or_buf
        UpperCamelCase_											=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        UpperCamelCase_											=							num_proc
        UpperCamelCase_											=							"""utf-8"""
        UpperCamelCase_											=							to_csv_kwargs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.to_csv_kwargs.pop("""path_or_buf"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""header"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""index"""  ,						__UpperCamelCase  )

        if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)  ):
               with open(self.path_or_buf  ,						"""wb+"""  ) as buffer:
                      UpperCamelCase_											=							self._write(file_obj=__UpperCamelCase  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        else:
               UpperCamelCase_											=							self._write(file_obj=self.path_or_buf  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        return written

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							args

        UpperCamelCase_											=							query_table(
            table=self.dataset.data  ,						key=slice(__UpperCamelCase  ,						offset + self.batch_size  )  ,						indices=self.dataset._indices  ,						)
        UpperCamelCase_											=							batch.to_pandas().to_csv(
            path_or_buf=__UpperCamelCase  ,						header=header if (offset == 0) else False  ,						index=__UpperCamelCase  ,						**__UpperCamelCase  )
        return csv_str.encode(self.encoding  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							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 CSV from Arrow format"""  ,						):
                      UpperCamelCase_											=							self._batch_csv((offset, header, index, to_csv_kwargs)  )
                      written += file_obj.write(__UpperCamelCase  )

        else:
               UpperCamelCase_							,					UpperCamelCase_											=							len(self.dataset  ), self.batch_size
               with multiprocessing.Pool(self.num_proc  ) as pool:
                      for csv_str in logging.tqdm(
                          pool.imap(
                              self._batch_csv  ,						[(offset, header, index, to_csv_kwargs) for offset in range(0  ,						__UpperCamelCase  ,						__UpperCamelCase  )]  ,						)  ,						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 CSV from Arrow format"""  ,						):
                             written += file_obj.write(__UpperCamelCase  )

        return written



