




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 		UpperCAmelCase       (    UpperCAmelCase__	,       unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       BertweetTokenizer
						lowerCAmelCase_							=       False






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_		   =					["I", "m", "V@@", "R@@", "r", "e@@"]
							snake_case_		   =					dict(zip(__lowercase	,     range(len(__lowercase						)						)						)						)
							snake_case_		   =					["#version: 0.2", "a m</w>"]
							snake_case_		   =					{"unk_token": "<unk>"}

							snake_case_		   =					os.path.join(self.tmpdirname	,     VOCAB_FILES_NAMES["vocab_file"]						)
							snake_case_		   =					os.path.join(self.tmpdirname	,     VOCAB_FILES_NAMES["merges_file"]						)
							with open(self.vocab_file	,     "w"	,     encoding="utf-8"						) as fp:
								for token in vocab_tokens:
									fp.write(f"{token} {vocab_tokens[token]}\n"						)
							with open(self.merges_file	,     "w"	,     encoding="utf-8"						) as fp:
								fp.write("\n".join(__lowercase						)						)






						def 	snake_case__						(						self							:					str	,     **__lowercase							:					Tuple						):






							"""simple docstring"""






							kwargs.update(self.special_tokens_map						)
							return BertweetTokenizer.from_pretrained(self.tmpdirname	,     **__lowercase						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					"I am VinAI Research"
							snake_case_		   =					"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					BertweetTokenizer(self.vocab_file	,     self.merges_file	,     **self.special_tokens_map						)
							snake_case_		   =					"I am VinAI Research"
							snake_case_		   =					"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							snake_case_		   =					tokenizer.tokenize(__lowercase						)
							self.assertListEqual(__lowercase	,     __lowercase						)

							snake_case_		   =					tokens + [tokenizer.unk_token]

							snake_case_		   =					[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)	,     __lowercase						)






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__    :					str					=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 		UpperCAmelCase       (    datasets.BuilderConfig      ):







						'''simple docstring'''





						lowerCAmelCase_							=       None







class 		UpperCAmelCase       (    datasets.ArrowBasedBuilder      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ArrowConfig






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							return datasets.DatasetInfo(features=self.config.features						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							if not self.config.data_files:
								raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"						)
							snake_case_		   =					dl_manager.download_and_extract(self.config.data_files						)
							if isinstance(__lowercase	,     (str, list, tuple)						):
								snake_case_		   =					data_files
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,     gen_kwargs={"files": files}						)]
							snake_case_		   =					[]
							for split_name, files in data_files.items():
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								# Infer features is they are stoed in the arrow schema
								if self.info.features is None:
									for file in itertools.chain.from_iterable(__lowercase						):
										with open(__lowercase	,     "rb"						) as f:
											snake_case_		   =					datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowercase						).schema						)
										break
								splits.append(datasets.SplitGenerator(name=__lowercase	,     gen_kwargs={"files": files}						)						)
							return splits






						def 	snake_case__						(						self							:					str	,     __lowercase							:					pa.Table						):






							"""simple docstring"""






							if self.info.features is not None:
								# more expensive cast to support nested features with keys in a different order
								# allows str <-> int/float or str to Audio for example
								snake_case_		   =					table_cast(__lowercase	,     self.info.features.arrow_schema						)
							return pa_table






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase						)						):
								with open(__lowercase	,     "rb"						) as f:
									try:
										for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowercase						)						):
											snake_case_		   =					pa.Table.from_batches([record_batch]						)
											# Uncomment for debugging (will print the Arrow table size and elements)
											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
											yield f"{file_idx}_{batch_idx}", self._cast_table(__lowercase						)
									except ValueError as e:
										logger.error(f"Failed to read file '{file}' with error {type(__lowercase						)}: {e}"						)
										raise
