





'''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


__UpperCAmelCase            =       datasets.utils.logging.get_logger(__name__)




@dataclass
class      UpperCamelCase__					(					datasets.BuilderConfig			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	None
					SCREAMING_SNAKE_CASE__				=	"utf-8"
					SCREAMING_SNAKE_CASE__				=	None
					SCREAMING_SNAKE_CASE__				=	None
					SCREAMING_SNAKE_CASE__				=	True  # deprecated
					SCREAMING_SNAKE_CASE__				=	None  # deprecated
					SCREAMING_SNAKE_CASE__				=	10 << 20  # 10MB
					SCREAMING_SNAKE_CASE__				=	None







class      UpperCamelCase__					(					datasets.ArrowBasedBuilder			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	JsonConfig


					def lowerCamelCase_			(						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"""						)
									SCREAMING_SNAKE_CASE :				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 lowerCamelCase_			(						self				:			Optional[Any]      ,						lowerCamelCase_				:			str						):

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


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			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						):
											SCREAMING_SNAKE_CASE :				Tuple		=			self.config.features.arrow_schema.field(lowerCamelCase_						).type
											SCREAMING_SNAKE_CASE :				List[Any]		=			pa_table.append_column(lowerCamelCase_      ,						pa.array([None] * len(lowerCamelCase_						)      ,						type=lowerCamelCase_						)						)
									# more expensive cast to support nested structures with keys in a different order
									# allows str <-> int/float or str to Audio for example
									SCREAMING_SNAKE_CASE :				int		=			table_cast(lowerCamelCase_      ,						self.config.features.arrow_schema						)
							return pa_table



					def lowerCamelCase_			(						self				:			Optional[int]      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase_						)						):
									# 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(lowerCamelCase_      ,						encoding=self.config.encoding      ,						errors=self.config.encoding_errors						) as f:
													SCREAMING_SNAKE_CASE :				Optional[int]		=			json.load(lowerCamelCase_						)

											# We keep only the field we are interested in
											SCREAMING_SNAKE_CASE :				List[Any]		=			dataset[self.config.field]

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

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










'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



