

import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Dict													= LongformerTokenizer
					UpperCAmelCase__:     Optional[Any]													= True
					UpperCAmelCase__:     Union[str, Any]													= LongformerTokenizerFast
					UpperCAmelCase__:     Optional[Any]													= True






					def 						__A    (				self    ):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						A__							:					Optional[int]       =	[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """\u0120""",
						    """\u0120l""",
						    """\u0120n""",
						    """\u0120lo""",
						    """\u0120low""",
						    """er""",
						    """\u0120lowest""",
						    """\u0120newer""",
						    """\u0120wider""",
						    """<unk>""",
						]
						A__							:					Optional[Any]       =	dict(zip(A__					, range(len(A__    )    )    )    )
						A__							:					Any       =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
						A__							:					str       =	{"""unk_token""": """<unk>"""}

						A__							:					Dict       =	os.path.join(self.tmpdirname					, VOCAB_FILES_NAMES["""vocab_file"""]    )
						A__							:					Optional[int]       =	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 						__A    (				self					, **A__    ):
						kwargs.update(self.special_tokens_map    )
						return self.tokenizer_class.from_pretrained(self.tmpdirname					, **A__    )






					def 						__A    (				self					, **A__    ):
						kwargs.update(self.special_tokens_map    )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					, **A__    )






					def 						__A    (				self					, A__    ):
						A__							:					Dict       =	"""lower newer"""
						A__							:					Dict       =	"""lower newer"""
						return input_text, output_text






					def 						__A    (				self    ):
						A__							:					List[Any]       =	self.tokenizer_class(self.vocab_file					, self.merges_file					, **self.special_tokens_map    )
						A__							:					Union[str, Any]       =	"""lower newer"""
						A__							:					Dict       =	["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
						A__							:					List[Any]       =	tokenizer.tokenize(A__    )  # , add_prefix_space=True)
						self.assertListEqual(A__					, A__    )

						A__							:					Dict       =	tokens + [tokenizer.unk_token]
						A__							:					List[str]       =	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A__    )					, A__    )






					def 						__A    (				self    ):
						A__							:					Tuple       =	self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("""Hello world!"""					, add_special_tokens=A__    )					, [0, 3_1414, 232, 328, 2]    )
						self.assertListEqual(
						    tokenizer.encode("""Hello world! cécé herlolip 418"""					, add_special_tokens=A__    )					, [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]					, )






					@slow
					def 						__A    (				self    ):
						A__							:					Tuple       =	self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""    )

						A__							:					Dict       =	tokenizer.encode("""sequence builders"""					, add_special_tokens=A__    )
						A__							:					Optional[int]       =	tokenizer.encode("""multi-sequence build"""					, add_special_tokens=A__    )

						A__							:					Any       =	tokenizer.encode(
						    """sequence builders"""					, add_special_tokens=A__					, add_prefix_space=A__    )
						A__							:					List[str]       =	tokenizer.encode(
						    """sequence builders"""					, """multi-sequence build"""					, add_special_tokens=A__					, add_prefix_space=A__    )

						A__							:					Tuple       =	tokenizer.build_inputs_with_special_tokens(A__    )
						A__							:					List[Any]       =	tokenizer.build_inputs_with_special_tokens(A__					, A__    )

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode






					def 						__A    (				self    ):
						A__							:					Any       =	self.get_tokenizer()

						A__							:					str       =	"""Encode this sequence."""
						A__							:					List[Any]       =	tokenizer.byte_encoder[""" """.encode("""utf-8"""    )[0]]

						# Testing encoder arguments
						A__							:					Optional[Any]       =	tokenizer.encode(A__					, add_special_tokens=A__					, add_prefix_space=A__    )
						A__							:					Optional[int]       =	tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
						self.assertNotEqual(A__					, A__    )

						A__							:					str       =	tokenizer.encode(A__					, add_special_tokens=A__					, add_prefix_space=A__    )
						A__							:					Optional[int]       =	tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
						self.assertEqual(A__					, A__    )

						tokenizer.add_special_tokens({"""bos_token""": """<s>"""}    )
						A__							:					Optional[int]       =	tokenizer.encode(A__					, add_special_tokens=A__    )
						A__							:					Any       =	tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
						self.assertNotEqual(A__					, A__    )

						# Testing spaces after special tokens
						A__							:					Dict       =	"""<mask>"""
						tokenizer.add_special_tokens(
						    {"""mask_token""": AddedToken(A__					, lstrip=A__					, rstrip=A__    )}    )  # mask token has a left space
						A__							:					Dict       =	tokenizer.convert_tokens_to_ids(A__    )

						A__							:					List[Any]       =	"""Encode <mask> sequence"""
						A__							:					Tuple       =	"""Encode <mask>sequence"""

						A__							:					Optional[int]       =	tokenizer.encode(A__    )
						A__							:					Union[str, Any]       =	encoded.index(A__    )
						A__							:					Optional[Any]       =	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
						self.assertEqual(A__					, A__    )

						A__							:					Optional[int]       =	tokenizer.encode(A__    )
						A__							:					Union[str, Any]       =	encoded.index(A__    )
						A__							:					Union[str, Any]       =	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
						self.assertNotEqual(A__					, A__    )






					def 						__A    (				self    ):
						pass






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

								A__							:					Any       =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
								A__							:					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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )
								self.assertSequenceEqual(tokens_r["""input_ids"""]					, [0, 250, 6, 5_0264, 3823, 487, 2_1992, 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>"""]    )






					def 						__A    (				self    ):
						for trim_offsets, add_prefix_space in itertools.product([True, False]					, repeat=2    ):
							A__							:					str       =	self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )

							A__							:					Dict       =	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
							A__							:					Optional[int]       =	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

							self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]					, A__    )

							self.assertEqual(post_processor_state["""add_prefix_space"""]					, A__    )
							self.assertEqual(post_processor_state["""trim_offsets"""]					, A__    )






					def 						__A    (				self    ):
						# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
						# `trim_offsets`
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
								A__							:					List[Any]       =	"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
								A__							:					int       =	F"""{text_of_1_token} {text_of_1_token}"""

								A__							:					Union[str, Any]       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )
								A__							:					Union[str, Any]       =	tokenizer_r(A__					, return_offsets_mapping=A__					, add_special_tokens=A__    )
								self.assertEqual(encoding.offset_mapping[0]					, (0, len(A__    ))    )
								self.assertEqual(
								    encoding.offset_mapping[1]					, (len(A__    ) + 1, len(A__    ) + 1 + len(A__    ))					, )

								A__							:					Optional[Any]       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )
								A__							:					Union[str, Any]       =	tokenizer_r(A__					, return_offsets_mapping=A__					, add_special_tokens=A__    )
								self.assertEqual(encoding.offset_mapping[0]					, (0, len(A__    ))    )
								self.assertEqual(
								    encoding.offset_mapping[1]					, (len(A__    ) + 1, len(A__    ) + 1 + len(A__    ))					, )

								A__							:					List[str]       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )
								A__							:					str       =	tokenizer_r(A__					, return_offsets_mapping=A__					, add_special_tokens=A__    )
								self.assertEqual(encoding.offset_mapping[0]					, (0, len(A__    ))    )
								self.assertEqual(
								    encoding.offset_mapping[1]					, (len(A__    ), len(A__    ) + 1 + len(A__    ))					, )

								A__							:					str       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )
								A__							:					Tuple       =	tokenizer_r(A__					, return_offsets_mapping=A__					, add_special_tokens=A__    )
								self.assertEqual(encoding.offset_mapping[0]					, (0, len(A__    ))    )
								self.assertEqual(
								    encoding.offset_mapping[1]					, (len(A__    ), len(A__    ) + 1 + len(A__    ))					, )

								A__							:					Any       =	F""" {text}"""

								# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
								#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
								# )
								# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
								# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
								# self.assertEqual(
								#     encoding.offset_mapping[1],
								#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
								# )

								A__							:					Optional[Any]       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )
								A__							:					Optional[Any]       =	tokenizer_r(A__					, return_offsets_mapping=A__					, add_special_tokens=A__    )
								self.assertEqual(encoding.offset_mapping[0]					, (1, 1 + len(A__    ))    )
								self.assertEqual(
								    encoding.offset_mapping[1]					, (1 + len(A__    ) + 1, 1 + len(A__    ) + 1 + len(A__    ))					, )

								A__							:					int       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )
								A__							:					Any       =	tokenizer_r(A__					, return_offsets_mapping=A__					, add_special_tokens=A__    )
								self.assertEqual(encoding.offset_mapping[0]					, (0, 1 + len(A__    ))    )
								self.assertEqual(
								    encoding.offset_mapping[1]					, (1 + len(A__    ), 1 + len(A__    ) + 1 + len(A__    ))					, )

								A__							:					int       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, use_fast=A__					, add_prefix_space=A__					, trim_offsets=A__    )
								A__							:					List[str]       =	tokenizer_r(A__					, return_offsets_mapping=A__					, add_special_tokens=A__    )
								self.assertEqual(encoding.offset_mapping[0]					, (0, 1 + len(A__    ))    )
								self.assertEqual(
								    encoding.offset_mapping[1]					, (1 + len(A__    ), 1 + len(A__    ) + 1 + len(A__    ))					, )






import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A_ :				Tuple						 =    datasets.utils.logging.get_logger(__name__)



@dataclass
class 					_a		(datasets.BuilderConfig       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[datasets.Features]													= None
					UpperCAmelCase__:     str													= "utf-8"
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     Optional[str]													= None
					UpperCAmelCase__:     bool													= True  # deprecated
					UpperCAmelCase__:     Optional[int]													= None  # deprecated
					UpperCAmelCase__:     int													= 10 << 20  # 10MB
					UpperCAmelCase__:     Optional[bool]													= None



class 					_a		(datasets.ArrowBasedBuilder       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= JsonConfig






					def 						__A    (				self    ):
						if self.config.block_size is not None:
							logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""    )
							A__							:					Union[str, Any]       =	self.config.block_size
						if self.config.use_threads is not True:
							logger.warning(
							    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""    )
						if self.config.newlines_in_values is not None:
							raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""    )
						return datasets.DatasetInfo(features=self.config.features    )






					def 						__A    (				self					, A__    ):
						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}"""    )
						A__							:					int       =	dl_manager.download_and_extract(self.config.data_files    )
						if isinstance(A__					, (str, list, tuple)    ):
							A__							:					Optional[Any]       =	data_files
							if isinstance(A__					, A__    ):
								A__							:					List[str]       =	[files]
							A__							:					int       =	[dl_manager.iter_files(A__    ) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={"""files""": files}    )]
						A__							:					List[str]       =	[]
						for split_name, files in data_files.items():
							if isinstance(A__					, A__    ):
								A__							:					Optional[int]       =	[files]
							A__							:					Optional[int]       =	[dl_manager.iter_files(A__    ) for file in files]
							splits.append(datasets.SplitGenerator(name=A__					, gen_kwargs={"""files""": files}    )    )
						return splits






					def 						__A    (				self					, A__    ):
						if self.config.features is not None:
							# adding missing columns
							for column_name in set(self.config.features    ) - set(pa_table.column_names    ):
								A__							:					Optional[Any]       =	self.config.features.arrow_schema.field(A__    ).type
								A__							:					str       =	pa_table.append_column(A__					, pa.array([None] * len(A__    )					, type=A__    )    )
							# more expensive cast to support nested structures with keys in a different order
							# allows str <-> int/float or str to Audio for example
							A__							:					Optional[int]       =	table_cast(A__					, self.config.features.arrow_schema    )
						return pa_table






					def 						__A    (				self					, A__    ):
						for file_idx, file in enumerate(itertools.chain.from_iterable(A__    )    ):
							# If the file is one json object and if we need to look at the list of items in one specific field
							if self.config.field is not None:
								with open(A__					, encoding=self.config.encoding					, errors=self.config.encoding_errors    ) as f:
									A__							:					Optional[Any]       =	json.load(A__    )

								# We keep only the field we are interested in
								A__							:					Optional[int]       =	dataset[self.config.field]

								# We accept two format: a list of dicts or a dict of lists
								if isinstance(A__					, (list, tuple)    ):
									A__							:					Union[str, Any]       =	set().union(*[row.keys() for row in dataset]    )
									A__							:					Any       =	{col: [row.get(A__    ) for row in dataset] for col in keys}
								else:
									A__							:					Any       =	dataset
								A__							:					Any       =	pa.Table.from_pydict(A__    )
								yield file_idx, self._cast_table(A__    )

							# If the file has one json object per line
							else:
								with open(A__					, """rb"""    ) as f:
									A__							:					List[str]       =	0
									# Use block_size equal to the chunk size divided by 32 to leverage multithreading
									# Set a default minimum value of 16kB if the chunk size is really small
									A__							:					List[str]       =	max(self.config.chunksize // 32					, 16 << 10    )
									A__							:					Any       =	(
									    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
									)
									while True:
										A__							:					Dict       =	f.read(self.config.chunksize    )
										if not batch:
											break
										# Finish current line
										try:
											batch += f.readline()
										except (AttributeError, io.UnsupportedOperation):
											batch += readline(A__    )
										# PyArrow only accepts utf-8 encoded bytes
										if self.config.encoding != "utf-8":
											A__							:					List[Any]       =	batch.decode(self.config.encoding					, errors=A__    ).encode("""utf-8"""    )
										try:
											while True:
												try:
													A__							:					str       =	paj.read_json(
													    io.BytesIO(A__    )					, read_options=paj.ReadOptions(block_size=A__    )    )
													break
												except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
													if (
													    isinstance(A__					, pa.ArrowInvalid    )
													    and "straddling" not in str(A__    )
													    or block_size > len(A__    )
													):
														raise
													else:
														# Increase the block size in case it was too small.
														# The block size will be reset for the next file.
														logger.debug(
														    F"""Batch of {len(A__    )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""    )
														block_size *= 2
										except pa.ArrowInvalid as e:
											try:
												with open(
												    A__					, encoding=self.config.encoding					, errors=self.config.encoding_errors    ) as f:
													A__							:					Optional[Any]       =	json.load(A__    )
											except json.JSONDecodeError:
												logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
												raise e
											# If possible, parse the file as a list of json objects and exit the loop
											if isinstance(A__					, A__    ):  # list is the only sequence type supported in JSON
												try:
													A__							:					str       =	set().union(*[row.keys() for row in dataset]    )
													A__							:					List[str]       =	{col: [row.get(A__    ) for row in dataset] for col in keys}
													A__							:					int       =	pa.Table.from_pydict(A__    )
												except (pa.ArrowInvalid, AttributeError) as e:
													logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
													raise ValueError(F"""Not able to read records in the JSON file at {file}."""    ) from None
												yield file_idx, self._cast_table(A__    )
												break
											else:
												logger.error(F"""Failed to read file '{file}' with error {type(A__    )}: {e}"""    )
												raise ValueError(
												    F"""Not able to read records in the JSON file at {file}. """
												    F"""You should probably indicate the field of the JSON file containing your records. """
												    F"""This JSON file contain the following fields: {str(list(dataset.keys()    )    )}. """
												    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """    ) from None
                        # 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 (file_idx, batch_idx), self._cast_table(A__    )
										batch_idx += 1



