


"""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     :   Union[str, Any]			    =			datasets.utils.logging.get_logger(__name__)
@dataclass
class   _UpperCamelCase  (   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				      =1_0 << 2_0  # 10MB
							__UpperCAmelCase						:    Optional[bool]				      =None
class   _UpperCamelCase  (   datasets.ArrowBasedBuilder		):
							'''simple docstring'''







							__UpperCAmelCase						:    str				      =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"	)
													__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 			snake_case				(     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}"	)
										__lowerCAmelCase				    =			dl_manager.download_and_extract(self.config.data_files	)
										if isinstance(__a    ,   (str, list, tuple)	):
													__lowerCAmelCase				    =			data_files
													if isinstance(__a    ,   __a	):
																__lowerCAmelCase				    =			[files]
													__lowerCAmelCase				    =			[dl_manager.iter_files(__a	) 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(__a    ,   __a	):
																__lowerCAmelCase				    =			[files]
													__lowerCAmelCase				    =			[dl_manager.iter_files(__a	) for file in files]
													splits.append(datasets.SplitGenerator(name=__a    ,   gen_kwargs={"files": files}	)	)
										return splits

							def 			snake_case				(     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	):
																__lowerCAmelCase				    =			self.config.features.arrow_schema.field(__a	).type
																__lowerCAmelCase				    =			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
													__lowerCAmelCase				    =			table_cast(__a    ,   self.config.features.arrow_schema	)
										return pa_table



							def 			snake_case				(     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:
																			__lowerCAmelCase				    =			json.load(__a	)

																# 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(__a    ,   (list, tuple)	):
																			__lowerCAmelCase				    =			set().union(*[row.keys() for row in dataset]	)
																			__lowerCAmelCase				    =			{col: [row.get(__a	) for row in dataset] for col in keys}
																else:
																			__lowerCAmelCase				    =			dataset
																__lowerCAmelCase				    =			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:
																			__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(__a	)
																						# PyArrow only accepts utf-8 encoded bytes
																						if self.config.encoding != "utf-8":
																									__lowerCAmelCase				    =			batch.decode(self.config.encoding    ,   errors=__a	).encode("utf-8"	)
																						try:
																									while True:
																												try:
																															__lowerCAmelCase				    =			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:
																															__lowerCAmelCase				    =			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:
																															__lowerCAmelCase				    =			set().union(*[row.keys() for row in dataset]	)
																															__lowerCAmelCase				    =			{col: [row.get(__a	) for row in dataset] for col in keys}
																															__lowerCAmelCase				    =			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








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :   Tuple			    =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Dict			    =			["BlenderbotTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BlenderbotForCausalLM",
	    "BlenderbotForConditionalGeneration",
	    "BlenderbotModel",
	    "BlenderbotPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "TFBlenderbotForConditionalGeneration",
	    "TFBlenderbotModel",
	    "TFBlenderbotPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxBlenderbotForConditionalGeneration",
	    "FlaxBlenderbotModel",
	    "FlaxBlenderbotPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	A     :   Optional[Any]			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




