


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:						List[str]   				=       datasets.utils.logging.get_logger(__name__)




@dataclass
class       lowercase_					(datasets.BuilderConfig   ):
				snake_case	    =None
				snake_case	    ="utf-8"
				snake_case	    =None
				snake_case	    =None
				snake_case	    =True  # deprecated
				snake_case	    =None  # deprecated
				snake_case	    =10 << 20  # 10MB
				snake_case	    =None







class       lowercase_					(datasets.ArrowBasedBuilder   ):
				snake_case	    =JsonConfig





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





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





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Optional[int]:
											for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_)):
																		# 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(lowercase_       ,  encoding=self.config.encoding       ,  errors=self.config.encoding_errors) as f:
																																a__      =json.load(lowercase_)

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

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase   :			List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

