




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


__a          =			logging.get_logger(__name__)

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()       )}       )
    lowerCAmelCase        =				field(
        metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}       )
    lowerCAmelCase        =				field(
        default=128    ,						metadata={
            '''help''': (
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )
        }    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Tuple       =      self.task_name.lower()


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''train'''
    lowerCAmelCase        =				'''dev'''
    lowerCAmelCase        =				'''test'''


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				42





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = Split.train       ,_SCREAMING_SNAKE_CASE = None       ,)			->		str:
         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'''       ,_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							Union[str, Any]       =      args
         UpperCAmelCase_      :							Optional[Any]       =      glue_processors[args.task_name]()
         UpperCAmelCase_      :							List[Any]       =      glue_output_modes[args.task_name]
         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              try:
                   UpperCAmelCase_      :							Any       =      Split[mode]
              except KeyError:
                   raise KeyError('''mode is not a valid split name'''  )
        # Load data features from cache or dataset file
         UpperCAmelCase_      :							Tuple       =      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}'''       ,)
         UpperCAmelCase_      :							str       =      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)
              UpperCAmelCase_      :							Any       =      label_list[2], label_list[1]
         UpperCAmelCase_      :							int       =      label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         UpperCAmelCase_      :							Optional[int]       =      cached_features_file + '''.lock'''
         with FileLock(_SCREAMING_SNAKE_CASE  ):
              if os.path.exists(_SCREAMING_SNAKE_CASE  ) and not args.overwrite_cache:
                   UpperCAmelCase_      :							List[str]       =      time.time()
                   UpperCAmelCase_      :							int       =      torch.load(_SCREAMING_SNAKE_CASE  )
                   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:
                        UpperCAmelCase_      :							Union[str, Any]       =      self.processor.get_dev_examples(args.data_dir  )
                   elif mode == Split.test:
                        UpperCAmelCase_      :							List[Any]       =      self.processor.get_test_examples(args.data_dir  )
                   else:
                        UpperCAmelCase_      :							int       =      self.processor.get_train_examples(args.data_dir  )
                   if limit_length is not None:
                        UpperCAmelCase_      :							Optional[Any]       =      examples[:limit_length]
                   UpperCAmelCase_      :							Optional[Any]       =      glue_convert_examples_to_features(
                       _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,max_length=args.max_seq_length       ,label_list=_SCREAMING_SNAKE_CASE       ,output_mode=self.output_mode       ,)
                   UpperCAmelCase_      :							List[str]       =      time.time()
                   torch.save(self.features       ,_SCREAMING_SNAKE_CASE  )
                   # ^ 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  )			->		Optional[Any]:
         return len(self.features  )





    def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		InputFeatures:
         return self.features[i]





    def 							a__				(				self  )			->		Tuple:
         return self.label_list





import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      r'''\w+[.]\d+'''
     UpperCAmelCase_      :							int       =      re.findall(_lowercase			,						_lowercase				)
     for pat in pats:
          UpperCAmelCase_      :							List[Any]       =      key.replace(_lowercase			,						'''_'''.join(pat.split('''.'''				)				)				)
     return key







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''scale''',)
     if (
         any('''norm''' in str_ for str_ in pt_tuple_key				)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''embedding''',)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCAmelCase_      :							List[Any]       =      pt_tensor.transpose(2			,						3			,						1			,						0				)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight":
          UpperCAmelCase_      :							Tuple       =      pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''weight''',)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''bias''',)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=42				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCAmelCase_      :							str       =      flax_model.init_weights(PRNGKey(_lowercase				)				)

     UpperCAmelCase_      :							List[Any]       =      flatten_dict(_lowercase				)
     UpperCAmelCase_      :							int       =      {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCAmelCase_      :							Optional[int]       =      rename_key(_lowercase				)
          UpperCAmelCase_      :							List[str]       =      tuple(renamed_pt_key.split('''.'''				)				)

          # Correctly rename weight parameters
          UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      rename_key_and_reshape_tensor(_lowercase			,						_lowercase			,						_lowercase				)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                        f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''				)

        # also add unexpected weight so that warning is thrown
          UpperCAmelCase_      :							int       =      jnp.asarray(_lowercase				)

     return unflatten_dict(_lowercase				)