



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case    :						Tuple  =						logging.get_logger(__name__)





@dataclass
class 							lowerCAmelCase__  :
					__A     :    List[str]           =						field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()						)}						)
					__A     :    Optional[Any]           =						field(
					    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}						)
					__A     :    List[Any]           =						field(
					    default=1_28					,							metadata={
					        'help': (
					            'The maximum total input sequence length after tokenization. Sequences longer '
					            'than this will be truncated, sequences shorter will be padded.'
					        )
					    }					,							)
					__A     :    Optional[int]           =						field(
					    default=_UpperCAmelCase					,							metadata={'help': 'Overwrite the cached training and evaluation sets'}						)







					def  _lowercase			(       self      :     List[str]):
											A__	:							Optional[Any]						    = self.task_name.lower()





class 							lowerCAmelCase__  (	_UpperCAmelCase						):
					__A     :    Any           =						'train'
					__A     :    Dict           =						'dev'
					__A     :    Any           =						'test'





class 							lowerCAmelCase__  (	_UpperCAmelCase						):
					__A     :    List[str]           =						42
					__A     :    Any           =						42
					__A     :    List[str]           =						42







					def __init__(       self      :     Union[str, Any]			,		_A      :     GlueDataTrainingArguments			,		_A      :     PreTrainedTokenizerBase			,		_A      :     Optional[int] = None			,		_A      :     Union[str, Split] = Split.train			,		_A      :     Optional[str] = None			,		):
											warnings.warn(
											    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
											    "library. You can have a look at this example script for pointers: "
											    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"			,		lowercase_			,		)
											A__	:							Optional[Any]						    = args
											A__	:							Union[str, Any]						    = glue_processors[args.task_name]()
											A__	:							Dict						    = glue_output_modes[args.task_name]
											if isinstance(lowercase_			,		lowercase_):
																	try:
																							A__	:							str						    = Split[mode]
																	except KeyError:
																							raise KeyError("mode is not a valid split name")
        # Load data features from cache or dataset file
											A__	:							Optional[Any]						    = os.path.join(
											    cache_dir if cache_dir is not None else args.data_dir			,		F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'			,		)
											A__	:							int						    = self.processor.get_labels()
											if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
											    "RobertaTokenizer",
											    "RobertaTokenizerFast",
											    "XLMRobertaTokenizer",
											    "BartTokenizer",
											    "BartTokenizerFast",
											):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	A__	:							Tuple						    = label_list[2], label_list[1]
											A__	:							Union[str, Any]						    = label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											A__	:							Union[str, Any]						    = cached_features_file + """.lock"""
											with FileLock(lowercase_):
																	if os.path.exists(lowercase_) and not args.overwrite_cache:
																							A__	:							Optional[Any]						    = time.time()
																							A__	:							str						    = torch.load(lowercase_)
																							logger.info(
																							    F'Loading features from cached file {cached_features_file} [took %.3f s]'			,		time.time() - start)
																	else:
																							logger.info(F'Creating features from dataset file at {args.data_dir}')

																							if mode == Split.dev:
																													A__	:							Optional[Any]						    = self.processor.get_dev_examples(args.data_dir)
																							elif mode == Split.test:
																													A__	:							Optional[int]						    = self.processor.get_test_examples(args.data_dir)
																							else:
																													A__	:							Dict						    = self.processor.get_train_examples(args.data_dir)
																							if limit_length is not None:
																													A__	:							Optional[int]						    = examples[:limit_length]
																							A__	:							Optional[int]						    = glue_convert_examples_to_features(
																							    lowercase_			,		lowercase_			,		max_length=args.max_seq_length			,		label_list=lowercase_			,		output_mode=self.output_mode			,		)
																							A__	:							Any						    = time.time()
																							torch.save(self.features			,		lowercase_)
																							# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																							logger.info(
																							    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]')







					def __len__(       self      :     Tuple):
											return len(self.features)







					def __getitem__(       self      :     Optional[int]			,		_A      :     Dict):
											return self.features[i]







					def  _lowercase			(       self      :     int):
											return self.label_list




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

snake_case    :						Optional[int]  =						get_logger()

snake_case    :						Optional[dict]  =						None




class 							lowerCAmelCase__  (	TensorFormatter[Mapping, 'jax.Array', Mapping]						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Optional[int]=None			,		_A      :     str=None			,		**_A      :     Tuple):
											super().__init__(features=_A)
											import jax
											from jaxlib.xla_client import Device

											if isinstance(_A			,		_A):
																	raise ValueError(
																	    F'Expected {device} to be a `str` not {type(_A)}, as `jaxlib.xla_extension.Device` '
																	    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
																	    "the device with `str()` to get its string identifier that will be internally mapped "
																	    "to the actual `jaxlib.xla_extension.Device`.")
											A__	:							Union[str, Any]						    = device if isinstance(_A			,		_A) else str(jax.devices()[0])
											# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
											# with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Any						    = self._map_devices_to_str()
											if self.device not in list(DEVICE_MAPPING.keys()):
																	logger.warning(
																	    F'Device with string identifier {self.device} not listed among the available '
																	    F'devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default '
																	    F'device: {str(jax.devices()[0])}.')
																	A__	:							str						    = str(jax.devices()[0])
											A__	:							List[Any]						    = jnp_array_kwargs







					@staticmethod
					def  _lowercase			(       ):
											import jax

											return {str(_A): device for device in jax.devices()}







					def  _lowercase			(       self      :     Tuple			,		_A      :     int):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		_A) and column:
																	if all(
																	    isinstance(_A			,		jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
																							return jnp.stack(_A			,		axis=0)
											return column







					def  _lowercase			(       self      :     List[str]			,		_A      :     Union[str, Any]):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		(str, bytes, type(_A))):
																	return value
											elif isinstance(_A			,		(np.character, np.ndarray)) and np.issubdtype(value.dtype			,		np.character):
																	return value.tolist()

											A__	:							Dict						    = {}

											if isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.integer):
																	# the default int precision depends on the jax config
																	# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
																	if jax.config.jax_enable_xaa:
																							A__	:							Union[str, Any]						    = {"dtype": jnp.intaa}
																	else:
																							A__	:							int						    = {"dtype": jnp.intaa}
											elif isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.floating):
																	A__	:							str						    = {"dtype": jnp.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(_A			,		PIL.Image.Image):
																							A__	:							Any						    = np.asarray(_A)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Optional[Any]						    = self._map_devices_to_str()

											with jax.default_device(DEVICE_MAPPING[self.device]):
																	# calling jnp.array on a np.ndarray does copy the data
																	# see https://github.com/google/jax/issues/4486
																	return jnp.array(_A			,		**{**default_dtype, **self.jnp_array_kwargs})







					def  _lowercase			(       self      :     Dict			,		_A      :     Any):
											import jax

											# support for torch, tf, jax etc.
											if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(_A			,		torch.Tensor):
																							return self._tensorize(data_struct.detach().cpu().numpy()[()])
											if hasattr(_A			,		"__array__") and not isinstance(_A			,		jax.Array):
																	A__	:							Any						    = data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_A			,		np.ndarray):
																	if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																							return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											elif isinstance(_A			,		(list, tuple)):
																	return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											return self._tensorize(_A)







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     dict):
											return map_nested(self._recursive_tensorize			,		_A			,		map_list=_A)







					def  _lowercase			(       self      :     List[str]			,		_A      :     pa.Table):
											A__	:							Dict						    = self.numpy_arrow_extractor().extract_row(_A)
											A__	:							int						    = self.python_features_decoder.decode_row(_A)
											return self.recursive_tensorize(_A)







					def  _lowercase			(       self      :     int			,		_A      :     pa.Table):
											A__	:							int						    = self.numpy_arrow_extractor().extract_column(_A)
											A__	:							Optional[int]						    = self.python_features_decoder.decode_column(_A			,		pa_table.column_names[0])
											A__	:							Dict						    = self.recursive_tensorize(_A)
											A__	:							List[Any]						    = self._consolidate(_A)
											return column







					def  _lowercase			(       self      :     Tuple			,		_A      :     pa.Table):
											A__	:							Any						    = self.numpy_arrow_extractor().extract_batch(_A)
											A__	:							str						    = self.python_features_decoder.decode_batch(_A)
											A__	:							Optional[Any]						    = self.recursive_tensorize(_A)
											for column_name in batch:
																	A__	:							Optional[int]						    = self._consolidate(batch[column_name])
											return batch