from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  42
  lowerCamelCase__   =  42


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



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   :     Tuple       =				datasets.utils.logging.get_logger(__name__)


@dataclass
class      lowerCamelCase    (datasets.BuilderConfig    ):
  """simple docstring"""







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



class      lowerCamelCase    (datasets.ArrowBasedBuilder    ):
  """simple docstring"""







  lowerCamelCase__   =  JsonConfig







  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        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_      =     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       __A   (							self       :				List[str]     ,   __magic_name__       :				str      )     ->		Tuple:
        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_      =     dl_manager.download_and_extract(self.config.data_files      )
        if isinstance(__magic_name__     ,   (str, list, tuple)      ):
              SCREAMING_SNAKE_CASE_      =     data_files
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,   gen_kwargs={"files": files}      )]
        SCREAMING_SNAKE_CASE_      =     []
        for split_name, files in data_files.items():
              if isinstance(__magic_name__     ,   __magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     [files]
              SCREAMING_SNAKE_CASE_      =     [dl_manager.iter_files(__magic_name__      ) for file in files]
              splits.append(datasets.SplitGenerator(name=__magic_name__     ,   gen_kwargs={"files": files}      )      )
        return splits







  def       __A   (							self       :				str     ,   __magic_name__       :				pa.Table      )     ->		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      ):
                    SCREAMING_SNAKE_CASE_      =     self.config.features.arrow_schema.field(__magic_name__      ).type
                    SCREAMING_SNAKE_CASE_      =     pa_table.append_column(__magic_name__     ,   pa.array([None] * len(__magic_name__      )     ,   type=__magic_name__      )      )
              # 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_      =     table_cast(__magic_name__     ,   self.config.features.arrow_schema      )
        return pa_table







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				List[str]      )     ->		int:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__      )      ):
              # 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(__magic_name__     ,   encoding=self.config.encoding     ,   errors=self.config.encoding_errors      ) as f:
                          SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )

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

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

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


