"""simple docstring"""




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


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

@dataclass
class    a_   (  datasets.BuilderConfig      ):
					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      ):
					UpperCamelCase_			:						Optional[int]      =     JsonConfig




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




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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  Union[str, Any]  ):
							for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__  )  ):
									# 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(snake_case__	,    encoding=self.config.encoding	,    errors=self.config.encoding_errors  ) as f:
													lowerCAmelCase__   = json.load(snake_case__  )

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

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

									# If the file has one json object per line
									else:
											with open(snake_case__	,    """rb"""  ) as f:
													lowerCAmelCase__   = 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
													lowerCAmelCase__   = max(self.config.chunksize // 32	,    16 << 10  )
													lowerCAmelCase__   = (
													    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
													)
													while True:
															lowerCAmelCase__   = f.read(self.config.chunksize  )
															if not batch:
																	break
															# Finish current line
															try:
																	batch += f.readline()
															except (AttributeError, io.UnsupportedOperation):
																	batch += readline(snake_case__  )
															# PyArrow only accepts utf-8 encoded bytes
															if self.config.encoding != "utf-8":
																	lowerCAmelCase__   = batch.decode(self.config.encoding	,    errors=snake_case__  ).encode("""utf-8"""  )
															try:
																	while True:
																			try:
																					lowerCAmelCase__   = paj.read_json(
																					    io.BytesIO(snake_case__  )	,    read_options=paj.ReadOptions(block_size=snake_case__  )  )
																					break
																			except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																					if (
																					    isinstance(snake_case__	,    pa.ArrowInvalid  )
																					    and "straddling" not in str(snake_case__  )
																					    or block_size > len(snake_case__  )
																					):
																							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(snake_case__  )} 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(
																			    snake_case__	,    encoding=self.config.encoding	,    errors=self.config.encoding_errors  ) as f:
																					lowerCAmelCase__   = json.load(snake_case__  )
																	except json.JSONDecodeError:
																			logger.error(F"""Failed to read file '{file}' with error {type(snake_case__  )}: {e}"""  )
																			raise e
																	# If possible, parse the file as a list of json objects and exit the loop
																	if isinstance(snake_case__	,    snake_case__  ):  # list is the only sequence type supported in JSON
																			try:
																					lowerCAmelCase__   = set().union(*[row.keys() for row in dataset]  )
																					lowerCAmelCase__   = {col: [row.get(snake_case__  ) for row in dataset] for col in keys}
																					lowerCAmelCase__   = pa.Table.from_pydict(snake_case__  )
																			except (pa.ArrowInvalid, AttributeError) as e:
																					logger.error(F"""Failed to read file '{file}' with error {type(snake_case__  )}: {e}"""  )
																					raise ValueError(F"""Not able to read records in the JSON file at {file}."""  ) from None
																			yield file_idx, self._cast_table(snake_case__  )
																			break
																	else:
																			logger.error(F"""Failed to read file '{file}' with error {type(snake_case__  )}: {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(snake_case__  )
															batch_idx += 1





"""simple docstring"""




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[int]      =     MgpstrTokenizer
					UpperCamelCase_			:						Optional[int]      =     False
					UpperCamelCase_			:						Tuple      =     {}
					UpperCamelCase_			:						List[str]      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							super().setUp()

							# fmt: off
							lowerCAmelCase__   = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
							# fmt: on
							lowerCAmelCase__   = dict(zip(snake_case__	,    range(len(snake_case__  )  )  )  )

							lowerCAmelCase__   = os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
							with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
									fp.write(json.dumps(snake_case__  ) + """\n"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    **snake_case__    :  Tuple  ):
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Dict  ):
							lowerCAmelCase__   = """tester"""
							lowerCAmelCase__   = """tester"""
							return input_text, output_text




					@unittest.skip("""MGP-STR always lower cases letters."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							pass




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = self.get_tokenizers(do_lower_case=snake_case__  )
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__   = """[SPECIAL_TOKEN]"""

											tokenizer.add_special_tokens({"""cls_token""": special_token}  )
											lowerCAmelCase__   = tokenizer.encode([special_token]	,    add_special_tokens=snake_case__  )
											self.assertEqual(len(snake_case__  )	,    1  )

											lowerCAmelCase__   = tokenizer.decode(snake_case__	,    skip_special_tokens=snake_case__  )
											self.assertTrue(special_token not in decoded  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.get_tokenizers()
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__ ,       lowerCAmelCase__   = self.get_input_output_texts(snake_case__  )

											lowerCAmelCase__   = tokenizer.tokenize(snake_case__  )
											lowerCAmelCase__   = tokenizer.convert_tokens_to_ids(snake_case__  )
											lowerCAmelCase__   = tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
											self.assertListEqual(snake_case__	,    snake_case__  )

											lowerCAmelCase__   = tokenizer.convert_ids_to_tokens(snake_case__  )
											self.assertNotEqual(len(snake_case__  )	,    0  )
											lowerCAmelCase__   = tokenizer.decode(snake_case__  )
											self.assertIsInstance(snake_case__	,    snake_case__  )

											self.assertEqual(text_a.replace(""" """	,    """"""  )	,    snake_case__  )




					@unittest.skip("""MGP-STR tokenizer only handles one sequence."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							pass




					@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							pass




