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     :  Optional[Any]					     =       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 :       Union[str, Any]   )  ->	str:
        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 :       Dict  ,     __magic_name__ :       List[str]   )  ->	str:
        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 :       Tuple  ,     __magic_name__ :       List[Any]   )  ->	List[Any]:
        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



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


