





'''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					:     Any												=datasets.utils.logging.get_logger(__name__)
@dataclass
class       a_   (				datasets.BuilderConfig							):
     __A      =			None
     __A      =			"utf-8"
     __A      =			None
     __A      =			None
     __A      =			True  # deprecated
     __A      =			None  # deprecated
     __A      =			10 << 20  # 10MB
     __A      =			None







class       a_   (				datasets.ArrowBasedBuilder							):
     __A      =			JsonConfig





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        if self.config.block_size is not None:
           logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"						)
           lowercase_			:Optional[int]    			= 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		:					Tuple	,      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}'						)
        lowercase_			:int    			= dl_manager.download_and_extract(self.config.data_files						)
        if isinstance(lowercase	,      (str, list, tuple)						):
           lowercase_			:List[str]    			= data_files
           if isinstance(lowercase	,      lowercase						):
              lowercase_			:Tuple    			= [files]
           lowercase_			:Optional[int]    			= [dl_manager.iter_files(lowercase						) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,      gen_kwargs={"files": files}						)]
        lowercase_			:List[Any]    			= []
        for split_name, files in data_files.items():
           if isinstance(lowercase	,      lowercase						):
              lowercase_			:str    			= [files]
           lowercase_			:Tuple    			= [dl_manager.iter_files(lowercase						) for file in files]
           splits.append(datasets.SplitGenerator(name=lowercase	,      gen_kwargs={"files": files}						)						)
        return splits





     def 							lowercase__      ( self		:					List[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						):
              lowercase_			:Dict    			= self.config.features.arrow_schema.field(lowercase						).type
              lowercase_			:List[str]    			= pa_table.append_column(lowercase	,      pa.array([None] * len(lowercase						)	,      type=lowercase						)						)
           # more expensive cast to support nested structures with keys in a different order
           # allows str <-> int/float or str to Audio for example
           lowercase_			:List[str]    			= table_cast(lowercase	,      self.config.features.arrow_schema						)
        return pa_table







     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					Union[str, Any]						):





        """simple docstring"""




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

              # We keep only the field we are interested in
              lowercase_			:List[Any]    			= dataset[self.config.field]

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

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









'''simple docstring'''


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)

lowerCAmelCase					:     Optional[int]												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Any    			= parser.parse_args()
   return args.f


class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= logging.StreamHandler(sys.stdout						)
        logger.addHandler(lowercase						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= 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(lowercase	,      "argv"	,      lowercase						):
              lowercase_			:int    			= run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(lowercase	,      0.6_66						)







     @slow
     @require_torch_non_multi_gpu
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:List[str]    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:Any    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)


