

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 __UpperCamelCase     (				datasets.BuilderConfig		):






					'''simple docstring'''



					__magic_name__        =   None
					__magic_name__        =   "utf-8"
					__magic_name__        =   None
					__magic_name__        =   None
					__magic_name__        =   True  # deprecated
					__magic_name__        =   None  # deprecated
					__magic_name__        =   1_0 << 2_0  # 10MB
					__magic_name__        =   None

class __UpperCamelCase     (				datasets.ArrowBasedBuilder		):






					'''simple docstring'''



					__magic_name__        =   JsonConfig





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





					def    _UpperCAmelCase    (				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__:	Optional[Any]									= self.config.features.arrow_schema.field(lowerCamelCase__						).type
								UpperCAmelCase__:	int									= 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
							UpperCAmelCase__:	List[Any]									= table_cast(lowerCamelCase__				,      self.config.features.arrow_schema						)
						return pa_table





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						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:
									UpperCAmelCase__:	str									= json.load(lowerCamelCase__						)

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

								# We accept two format: a list of dicts or a dict of lists
								if isinstance(lowerCamelCase__				,      (list, tuple)						):
									UpperCAmelCase__:	Optional[Any]									= set().union(*[row.keys() for row in dataset]						)
									UpperCAmelCase__:	List[str]									= {col: [row.get(lowerCamelCase__						) for row in dataset] for col in keys}
								else:
									UpperCAmelCase__:	Union[str, Any]									= dataset
								UpperCAmelCase__:	Union[str, Any]									= 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:
									UpperCAmelCase__:	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__:	int									= max(self.config.chunksize // 3_2				,      1_6 << 1_0						)
									UpperCAmelCase__:	str									= (
									    self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
									)
									while True:
										UpperCAmelCase__:	str									= 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":
											UpperCAmelCase__:	List[str]									= batch.decode(self.config.encoding				,      errors=lowerCamelCase__						).encode("utf-8"						)
										try:
											while True:
												try:
													UpperCAmelCase__:	str									= 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:
													UpperCAmelCase__:	int									= 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:
													UpperCAmelCase__:	int									= set().union(*[row.keys() for row in dataset]						)
													UpperCAmelCase__:	int									= {col: [row.get(lowerCamelCase__						) for row in dataset] for col in keys}
													UpperCAmelCase__:	Tuple									= 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


import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_lowerCAmelCase  :      int			 ="""scheduler_config.json"""

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   1
					__magic_name__        =   2
					__magic_name__        =   3
					__magic_name__        =   4
					__magic_name__        =   5
					__magic_name__        =   6
					__magic_name__        =   7
					__magic_name__        =   8
					__magic_name__        =   9
					__magic_name__        =   1_0
					__magic_name__        =   1_1
					__magic_name__        =   1_2
					__magic_name__        =   1_3
					__magic_name__        =   1_4

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   SCHEDULER_CONFIG_NAME
					__magic_name__        =   []
					__magic_name__        =   True





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__				,      subfolder=lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      return_commit_hash=lowerCamelCase__				,      **lowerCamelCase__				,      )
						return cls.from_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = False				,      **lowerCamelCase__						):
						self.save_config(save_directory=lowerCamelCase__				,      push_to_hub=lowerCamelCase__				,      **lowerCamelCase__						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._get_compatibles()





					@classmethod
					def    _UpperCAmelCase    (				cls						):
						UpperCAmelCase__:	List[str]									= list(set([cls.__name__] + cls._compatibles						)						)
						UpperCAmelCase__:	Union[str, Any]									= importlib.import_module(__name__.split("."						)[0]						)
						UpperCAmelCase__:	int									= [
						    getattr(lowerCamelCase__				,      lowerCamelCase__						) for c in compatible_classes_str if hasattr(lowerCamelCase__				,      lowerCamelCase__						)
						]
						return compatible_classes