




'''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


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


@dataclass
class        UpperCamelCase_					(				datasets.BuilderConfig  ):

    '''simple docstring'''






    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   '''utf-8'''
    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   True  # deprecated
    UpperCAmelCase__													=   None  # deprecated
    UpperCAmelCase__													=   10 << 20  # 10MB
    UpperCAmelCase__													=   None


class        UpperCamelCase_					(				datasets.ArrowBasedBuilder  ):

    '''simple docstring'''






    UpperCAmelCase__													=   JsonConfig


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			str)		->Optional[Any]:





           '''simple docstring'''

           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(UpperCAmelCase__      ,				(str, list, tuple)):
                  A__   				=					data_files
                  if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                         A__   				=					[files]
                  A__   				=					[dl_manager.iter_files(UpperCAmelCase__) 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(UpperCAmelCase__      ,				UpperCAmelCase__):
                         A__   				=					[files]
                  A__   				=					[dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  splits.append(datasets.SplitGenerator(name=UpperCAmelCase__      ,				gen_kwargs={'''files''': files}))
           return splits


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			pa.Table)		->pa.Table:





           '''simple docstring'''

           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(UpperCAmelCase__).type
                         A__   				=					pa_table.append_column(UpperCAmelCase__      ,				pa.array([None] * len(UpperCAmelCase__)      ,				type=UpperCAmelCase__))
                  # 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(UpperCAmelCase__      ,				self.config.features.arrow_schema)
           return pa_table





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any])		->List[str]:





           '''simple docstring'''

           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__)):
                  # 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(UpperCAmelCase__      ,				encoding=self.config.encoding      ,				errors=self.config.encoding_errors) as f:
                                A__   				=					json.load(UpperCAmelCase__)

                         # 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(UpperCAmelCase__      ,				(list, tuple)):
                                A__   				=					set().union(*[row.keys() for row in dataset])
                                A__   				=					{col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                         else:
                                A__   				=					dataset
                         A__   				=					pa.Table.from_pydict(UpperCAmelCase__)
                         yield file_idx, self._cast_table(UpperCAmelCase__)

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








import unittest

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


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


