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







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







class __snake_case    (						datasets.ArrowBasedBuilder		):
       lowerCAmelCase_														=							JsonConfig




       def      __a						(   self      :    Dict				):



         """simple docstring"""






         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      :    Union[str, Any]	,     _lowercase      :    Dict				):



         """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}"""				)
         SCREAMING_SNAKE_CASE__      							=				dl_manager.download_and_extract(self.config.data_files				)
         if isinstance(_a	,     (str, list, tuple)				):
           SCREAMING_SNAKE_CASE__      							=				data_files
           if isinstance(_a	,     _a				):
             SCREAMING_SNAKE_CASE__      							=				[files]
           SCREAMING_SNAKE_CASE__      							=				[dl_manager.iter_files(_a				) 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(_a	,     _a				):
             SCREAMING_SNAKE_CASE__      							=				[files]
           SCREAMING_SNAKE_CASE__      							=				[dl_manager.iter_files(_a				) for file in files]
           splits.append(datasets.SplitGenerator(name=_a	,     gen_kwargs={"""files""": files}				)				)
         return splits




       def      __a						(   self      :    str	,     _lowercase      :    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				):
             SCREAMING_SNAKE_CASE__      							=				self.config.features.arrow_schema.field(_a				).type
             SCREAMING_SNAKE_CASE__      							=				pa_table.append_column(_a	,     pa.array([None] * len(_a				)	,     type=_a				)				)
           # 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(_a	,     self.config.features.arrow_schema				)
         return pa_table




       def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Dict				):



         """simple docstring"""






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

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

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

import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
   from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
   from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
   from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__lowerCamelCase      :			Tuple							       =		DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__lowerCamelCase      :			Optional[Any]							       =		'''main'''
# Default branch name
__lowerCamelCase      :			Optional[int]							       =		'''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
__lowerCamelCase      :			Any							       =		'''aaaaaaa'''
# This commit does not exist, so we should 404.

__lowerCamelCase      :			List[str]							       =		'''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
__lowerCamelCase      :			List[Any]							       =		'''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''



@contextlib.contextmanager
def      __SCREAMING_SNAKE_CASE  ( )							-> str:







  """simple docstring"""





  print("""Welcome!"""     )
  yield
  print("""Bye!"""     )



@contextlib.contextmanager
def      __SCREAMING_SNAKE_CASE  ( )							-> Any:







  """simple docstring"""





  print("""Bonjour!"""     )
  yield
  print("""Au revoir!"""     )







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     assert transformers.__spec__ is not None
     assert importlib.util.find_spec("""transformers"""				) is not None







class __snake_case    (						unittest.TestCase		):




   @unittest.mock.patch("""sys.stdout"""	,     new_callable=io.StringIO				)
   def      __a						(   self      :    List[str]	,     _lowercase      :    str				):



     """simple docstring"""






     with ContextManagers([]				):
       print("""Transformers are awesome!"""				)
     # The print statement adds a new line at the end of the output
     self.assertEqual(mock_stdout.getvalue()	,     """Transformers are awesome!\n"""				)




   @unittest.mock.patch("""sys.stdout"""	,     new_callable=io.StringIO				)
   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    str				):



     """simple docstring"""






     with ContextManagers([context_en()]				):
       print("""Transformers are awesome!"""				)
     # The output should be wrapped with an English welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()	,     """Welcome!\nTransformers are awesome!\nBye!\n"""				)




   @unittest.mock.patch("""sys.stdout"""	,     new_callable=io.StringIO				)
   def      __a						(   self      :    Tuple	,     _lowercase      :    Dict				):



     """simple docstring"""






     with ContextManagers([context_fr(), context_en()]				):
       print("""Transformers are awesome!"""				)
     # The output should be wrapped with an English and French welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()	,     """Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"""				)




   @require_torch
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""labels""", """next_sentence_label"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""start_positions""", """end_positions"""]				)







     class __snake_case    (						lowerCamelCase_		):
        pass





     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)




   @require_tf
   def      __a						(   self      :    Any				):



     """simple docstring"""






     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""labels""", """next_sentence_label"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""start_positions""", """end_positions"""]				)







     class __snake_case    (						lowerCamelCase_		):
        pass





     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)




   @require_flax
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     self.assertEqual(find_labels(_lowercase				)	,     []				)
     self.assertEqual(find_labels(_lowercase				)	,     []				)
     self.assertEqual(find_labels(_lowercase				)	,     []				)







     class __snake_case    (						lowerCamelCase_		):
        pass





     self.assertEqual(find_labels(_lowercase				)	,     []				)
