

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


_SCREAMING_SNAKE_CASE     :  str   			=			datasets.utils.logging.get_logger(__name__)






@dataclass
class        UpperCAmelCase__		(       datasets.BuilderConfig       ):






  """simple docstring"""


  a           =					None
  a           =					"utf-8"
  a           =					None
  a           =					None
  a           =					True  # deprecated
  a           =					None  # deprecated
  a           =					10 << 20  # 10MB
  a           =					None






class        UpperCAmelCase__		(       datasets.ArrowBasedBuilder       ):






  """simple docstring"""


  a           =					JsonConfig






  def       lowercase_		(							self						:		Dict					)		->							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       lowercase_		(							self						:		Optional[Any]	,   __lowerCamelCase						:		str					)		->							Any:
       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(__lowerCamelCase	,   (str, list, tuple)					):
            SCREAMING_SNAKE_CASE__    =    data_files
            if isinstance(__lowerCamelCase	,   __lowerCamelCase					):
                 SCREAMING_SNAKE_CASE__    =    [files]
            SCREAMING_SNAKE_CASE__    =    [dl_manager.iter_files(__lowerCamelCase					) 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(__lowerCamelCase	,   __lowerCamelCase					):
                 SCREAMING_SNAKE_CASE__    =    [files]
            SCREAMING_SNAKE_CASE__    =    [dl_manager.iter_files(__lowerCamelCase					) for file in files]
            splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,   gen_kwargs={'''files''': files}					)					)
       return splits






  def       lowercase_		(							self						:		str	,   __lowerCamelCase						:		Optional[int]					)		->							Optional[int]:
       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(__lowerCamelCase					).type
                 SCREAMING_SNAKE_CASE__    =    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
            SCREAMING_SNAKE_CASE__    =    table_cast(__lowerCamelCase	,   self.config.features.arrow_schema					)
       return pa_table







  def       lowercase_		(							self						:		Optional[Any]	,   __lowerCamelCase						:		List[Any]					)		->							int:
       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:
                      SCREAMING_SNAKE_CASE__    =    json.load(__lowerCamelCase					)

                 # 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(__lowerCamelCase	,   (list, tuple)					):
                      SCREAMING_SNAKE_CASE__    =    set().union(*[row.keys() for row in dataset]					)
                      SCREAMING_SNAKE_CASE__    =    {col: [row.get(__lowerCamelCase					) for row in dataset] for col in keys}
                 else:
                      SCREAMING_SNAKE_CASE__    =    dataset
                 SCREAMING_SNAKE_CASE__    =    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:
                      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(__lowerCamelCase					)
                           # PyArrow only accepts utf-8 encoded bytes
                           if self.config.encoding != "utf-8":
                                SCREAMING_SNAKE_CASE__    =    batch.decode(self.config.encoding	,   errors=__lowerCamelCase					).encode('''utf-8'''					)
                           try:
                                while True:
                                     try:
                                          SCREAMING_SNAKE_CASE__    =    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:
                                          SCREAMING_SNAKE_CASE__    =    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:
                                          SCREAMING_SNAKE_CASE__    =    set().union(*[row.keys() for row in dataset]					)
                                          SCREAMING_SNAKE_CASE__    =    {col: [row.get(__lowerCamelCase					) for row in dataset] for col in keys}
                                          SCREAMING_SNAKE_CASE__    =    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 argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						get_gpu_count()

        if n_gpu > 1:
               pass
               # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
               # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
               # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
               # cmd = [sys.executable] + distributed_args + args
               # execute_subprocess_async(cmd, env=self.get_env())
               # XXX: test the results - need to save them first into .json file
        else:
               args.insert(0,'''run_glue_deebert.py''' )
               with patch.object(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							self ):
        A__											=						'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )





