


'''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


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


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


class 						_snake_case   (       datasets.ArrowBasedBuilder		):
					lowerCAmelCase :Tuple						=   JsonConfig




					def 			snake_case__  ( self):







								if self.config.block_size is not None:
											logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead""")
											UpperCAmelCase__					:   List[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 			snake_case__  ( self							,							_lowerCamelCase):







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




					def 			snake_case__  ( self							,							_lowerCamelCase):







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




					def 			snake_case__  ( self							,							_lowerCamelCase):







								for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_)):
											# 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(UpperCamelCase_							,							encoding=self.config.encoding							,							errors=self.config.encoding_errors) as f:
																	UpperCAmelCase__					:   List[str]       				=       json.load(UpperCamelCase_)

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

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

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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)