
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


_UpperCAmelCase :			Any              =	datasets.utils.logging.get_logger(__name__)


@dataclass
class     lowercase (		datasets.BuilderConfig       ):
		__lowercase	:	Optional[datasets.Features]     					=    None
		__lowercase	:	str     					=    "utf-8"
		__lowercase	:	Optional[str]     					=    None
		__lowercase	:	Optional[str]     					=    None
		__lowercase	:	bool     					=    True  # deprecated
		__lowercase	:	Optional[int]     					=    None  # deprecated
		__lowercase	:	int     					=    10 << 20  # 10MB
		__lowercase	:	Optional[bool]     					=    None






class     lowercase (		datasets.ArrowBasedBuilder       ):
		__lowercase	:	int     					=    JsonConfig



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				if self.config.block_size is not None:
						logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'	)
						UpperCamelCase          =					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 __UpperCamelCase			(			self    ,				A_	)  -> 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}'''	)
				UpperCamelCase          =					dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(A_    ,				(str, list, tuple)	):
						UpperCamelCase          =					data_files
						if isinstance(A_    ,				A_	):
								UpperCamelCase          =					[files]
						UpperCamelCase          =					[dl_manager.iter_files(A_	) for file in files]
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,				gen_kwargs={'files': files}	)]
				UpperCamelCase          =					[]
				for split_name, files in data_files.items():
						if isinstance(A_    ,				A_	):
								UpperCamelCase          =					[files]
						UpperCamelCase          =					[dl_manager.iter_files(A_	) for file in files]
						splits.append(datasets.SplitGenerator(name=A_    ,				gen_kwargs={'files': files}	)	)
				return splits



		def __UpperCamelCase			(			self    ,				A_	)  -> pa.Table:
				"""simple docstring"""
				if self.config.features is not None:
						# adding missing columns
						for column_name in set(self.config.features	) - set(pa_table.column_names	):
								UpperCamelCase          =					self.config.features.arrow_schema.field(A_	).type
								UpperCamelCase          =					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
						UpperCamelCase          =					table_cast(A_    ,				self.config.features.arrow_schema	)
				return pa_table



		def __UpperCamelCase			(			self    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				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:
										UpperCamelCase          =					json.load(A_	)

								# We keep only the field we are interested in
								UpperCamelCase          =					dataset[self.config.field]

								# We accept two format: a list of dicts or a dict of lists
								if isinstance(A_    ,				(list, tuple)	):
										UpperCamelCase          =					set().union(*[row.keys() for row in dataset]	)
										UpperCamelCase          =					{col: [row.get(A_	) for row in dataset] for col in keys}
								else:
										UpperCamelCase          =					dataset
								UpperCamelCase          =					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:
										UpperCamelCase          =					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
										UpperCamelCase          =					max(self.config.chunksize // 32    ,				16 << 10	)
										UpperCamelCase          =					(
										    self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
										)
										while True:
												UpperCamelCase          =					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":
														UpperCamelCase          =					batch.decode(self.config.encoding    ,				errors=A_	).encode('utf-8'	)
												try:
														while True:
																try:
																		UpperCamelCase          =					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:
																		UpperCamelCase          =					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:
																		UpperCamelCase          =					set().union(*[row.keys() for row in dataset]	)
																		UpperCamelCase          =					{col: [row.get(A_	) for row in dataset] for col in keys}
																		UpperCamelCase          =					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


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase :			Union[str, Any]              =	get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")


@require_sentencepiece
@require_tokenizers
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    GPTSwaTokenizer
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Union[str, Any]     					=    True
		__lowercase	:	Tuple     					=    False



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCamelCase          =					GPTSwaTokenizer(A_    ,				eos_token='<unk>'    ,				bos_token='<unk>'    ,				pad_token='<unk>'	)

				tokenizer.save_pretrained(self.tmpdirname	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'This is a test'
				UpperCamelCase          =					'This is a test'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					'<s>'
				UpperCamelCase          =					1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_	)    ,				A_	)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_	)    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					list(self.get_tokenizer().get_vocab().keys()	)

				self.assertEqual(vocab_keys[0]    ,				'<unk>'	)
				self.assertEqual(vocab_keys[1]    ,				'<s>'	)
				self.assertEqual(vocab_keys[-1]    ,				'j'	)
				self.assertEqual(len(A_	)    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)

				UpperCamelCase          =					tokenizer.tokenize('This is a test'	)
				self.assertListEqual(A_    ,				['▁This', '▁is', '▁a', '▁t', 'est']	)

				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				[465, 287, 265, 631, 842]	)

				UpperCamelCase          =					tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']    ,				)
				# fmt: on

				UpperCamelCase          =					tokenizer.convert_tokens_to_ids(A_	)
				self.assertListEqual(
				    A_    ,				[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]    ,				)

				UpperCamelCase          =					tokenizer.convert_ids_to_tokens(A_	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']	)
				# fmt: on



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)
				UpperCamelCase          =					['This is a test', 'I was born in 92000, and this is falsé.']
				UpperCamelCase          =					[
				    [465, 287, 265, 631, 842],
				    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
				]

				# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
				for text, expected_ids in zip(A_    ,				A_	):
						self.assertListEqual(tokenizer.encode_fast(A_	)    ,				A_	)

				# Test that decode_fast returns the input text
				for text, token_ids in zip(A_    ,				A_	):
						self.assertEqual(tokenizer.decode_fast(A_	)    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[
				    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
				    'Hey there, how are you doing this fine day?',
				    'This is a text with a trailing spaces followed by a dot     .',
				    'Häj sväjs lillebrör! =)',
				    'Det är inget fel på Mr. Cool',
				]

				# fmt: off
				UpperCamelCase          =					{'input_ids': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
				# fmt: on
				self.tokenizer_integration_test_util(
				    expected_encoding=A_    ,				model_name='AI-Sweden/gpt-sw3-126m'    ,				sequences=A_    ,				)
