




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_UpperCAmelCase		:  List[Any]	=   logging.getLogger(__name__)




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  42
       _a           =  42







@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  42
       _a           =  None
       _a           =  None







class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  '''train'''
       _a           =  '''dev'''
       _a           =  '''test'''







class       __lowerCAmelCase				:






       @staticmethod
       def     SCREAMING_SNAKE_CASE			(      _lowerCAmelCase:    Optional[int]				,    _lowerCAmelCase:    Union[Split, str] ):
         raise NotImplementedError






       @staticmethod
       def     SCREAMING_SNAKE_CASE			(      _lowerCAmelCase:    str ):
         raise NotImplementedError






       @staticmethod
       def     SCREAMING_SNAKE_CASE			(      _lowerCAmelCase:    List[InputExample]				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    PreTrainedTokenizer				,    _lowerCAmelCase:    int=False				,    _lowerCAmelCase:    str="[CLS]"				,    _lowerCAmelCase:    int=1				,    _lowerCAmelCase:    Optional[int]="[SEP]"				,    _lowerCAmelCase:    Optional[Any]=False				,    _lowerCAmelCase:    Dict=False				,    _lowerCAmelCase:    str=0				,    _lowerCAmelCase:    Tuple=0				,    _lowerCAmelCase:    Dict=-1_00				,    _lowerCAmelCase:    Tuple=0				,    _lowerCAmelCase:    Dict=True				,    ):
         lowercase			:Optional[Any]							=  {label: i for i, label in enumerate(_lowerCAmelCase )}

         lowercase			:Union[str, Any]							=  []
         for ex_index, example in enumerate(_lowerCAmelCase ):
           if ex_index % 1_00_00 == 0:
             logger.info("Writing example %d of %d"				,    _lowerCAmelCase				,    len(_lowerCAmelCase ) )

           lowercase			:str							=  []
           lowercase			:List[str]							=  []
           for word, label in zip(example.words				,    example.labels ):
             lowercase			:str							=  tokenizer.tokenize(_lowerCAmelCase )

             # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
             if len(_lowerCAmelCase ) > 0:
               tokens.extend(_lowerCAmelCase )
               # Use the real label id for the first token of the word, and padding ids for the remaining tokens
               label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_lowerCAmelCase ) - 1) )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
           lowercase			:int							=  tokenizer.num_special_tokens_to_add()
           if len(_lowerCAmelCase ) > max_seq_length - special_tokens_count:
             lowercase			:Tuple							=  tokens[: (max_seq_length - special_tokens_count)]
             lowercase			:Union[str, Any]							=  label_ids[: (max_seq_length - special_tokens_count)]

           # The convention in BERT is:
           # (a) For sequence pairs:
           #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
           #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
           # (b) For single sequences:
           #  tokens:   [CLS] the dog is hairy . [SEP]
           #  type_ids:   0   0   0   0  0     0   0
           #
           # Where "type_ids" are used to indicate whether this is the first
           # sequence or the second sequence. The embedding vectors for `type=0` and
           # `type=1` were learned during pre-training and are added to the wordpiece
           # embedding vector (and position vector). This is not *strictly* necessary
           # since the [SEP] token unambiguously separates the sequences, but it makes
           # it easier for the model to learn the concept of sequences.
           #
           # For classification tasks, the first vector (corresponding to [CLS]) is
           # used as the "sentence vector". Note that this only makes sense because
           # the entire model is fine-tuned.
           tokens += [sep_token]
           label_ids += [pad_token_label_id]
           if sep_token_extra:
             # roberta uses an extra separator b/w pairs of sentences
             tokens += [sep_token]
             label_ids += [pad_token_label_id]
           lowercase			:Any							=  [sequence_a_segment_id] * len(_lowerCAmelCase )

           if cls_token_at_end:
             tokens += [cls_token]
             label_ids += [pad_token_label_id]
             segment_ids += [cls_token_segment_id]
           else:
             lowercase			:Union[str, Any]							=  [cls_token] + tokens
             lowercase			:Union[str, Any]							=  [pad_token_label_id] + label_ids
             lowercase			:Dict							=  [cls_token_segment_id] + segment_ids

           lowercase			:List[str]							=  tokenizer.convert_tokens_to_ids(_lowerCAmelCase )

           # The mask has 1 for real tokens and 0 for padding tokens. Only real
           # tokens are attended to.
           lowercase			:List[Any]							=  [1 if mask_padding_with_zero else 0] * len(_lowerCAmelCase )

           # Zero-pad up to the sequence length.
           lowercase			:List[str]							=  max_seq_length - len(_lowerCAmelCase )
           if pad_on_left:
             lowercase			:str							=  ([pad_token] * padding_length) + input_ids
             lowercase			:str							=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
             lowercase			:Union[str, Any]							=  ([pad_token_segment_id] * padding_length) + segment_ids
             lowercase			:int							=  ([pad_token_label_id] * padding_length) + label_ids
           else:
             input_ids += [pad_token] * padding_length
             input_mask += [0 if mask_padding_with_zero else 1] * padding_length
             segment_ids += [pad_token_segment_id] * padding_length
             label_ids += [pad_token_label_id] * padding_length

           assert len(_lowerCAmelCase ) == max_seq_length
           assert len(_lowerCAmelCase ) == max_seq_length
           assert len(_lowerCAmelCase ) == max_seq_length
           assert len(_lowerCAmelCase ) == max_seq_length

           if ex_index < 5:
             logger.info("*** Example ***" )
             logger.info("guid: %s"				,    example.guid )
             logger.info("tokens: %s"				,    " ".join([str(_lowerCAmelCase ) for x in tokens] ) )
             logger.info("input_ids: %s"				,    " ".join([str(_lowerCAmelCase ) for x in input_ids] ) )
             logger.info("input_mask: %s"				,    " ".join([str(_lowerCAmelCase ) for x in input_mask] ) )
             logger.info("segment_ids: %s"				,    " ".join([str(_lowerCAmelCase ) for x in segment_ids] ) )
             logger.info("label_ids: %s"				,    " ".join([str(_lowerCAmelCase ) for x in label_ids] ) )

           if "token_type_ids" not in tokenizer.model_input_names:
             lowercase			:Union[str, Any]							=  None

           features.append(
               InputFeatures(
                   input_ids=_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    token_type_ids=_lowerCAmelCase				,    label_ids=_lowerCAmelCase ) )
         return features


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset




     class       __lowerCAmelCase				(							lowerCAmelCase):
            _a           =  42
            _a           =  nn.CrossEntropyLoss().ignore_index






            def __init__(      self:    Tuple				,    _lowerCAmelCase:    TokenClassificationTask				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    PreTrainedTokenizer				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[int] = None				,    _lowerCAmelCase:    int=False				,    _lowerCAmelCase:    Split = Split.train				,    ):
              # Load data features from cache or dataset file
              lowercase			:Any							=  os.path.join(
                  _lowerCAmelCase				,    "cached_{}_{}_{}".format(mode.value				,    tokenizer.__class__.__name__				,    str(_lowerCAmelCase ) )				,    )

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              lowercase			:Any							=  cached_features_file + ".lock"
              with FileLock(_lowerCAmelCase ):
                if os.path.exists(_lowerCAmelCase ) and not overwrite_cache:
                  logger.info(F"Loading features from cached file {cached_features_file}" )
                  lowercase			:List[str]							=  torch.load(_lowerCAmelCase )
                else:
                  logger.info(F"Creating features from dataset file at {data_dir}" )
                  lowercase			:str							=  token_classification_task.read_examples_from_file(_lowerCAmelCase				,    _lowerCAmelCase )
                  # TODO clean up all this to leverage built-in features of tokenizers
                  lowercase			:Any							=  token_classification_task.convert_examples_to_features(
                      _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    cls_token_at_end=bool(model_type in ["xlnet"] )				,    cls_token=tokenizer.cls_token				,    cls_token_segment_id=2 if model_type in ["xlnet"] else 0				,    sep_token=tokenizer.sep_token				,    sep_token_extra=_lowerCAmelCase				,    pad_on_left=bool(tokenizer.padding_side == "left" )				,    pad_token=tokenizer.pad_token_id				,    pad_token_segment_id=tokenizer.pad_token_type_id				,    pad_token_label_id=self.pad_token_label_id				,    )
                  logger.info(F"Saving features into cached file {cached_features_file}" )
                  torch.save(self.features				,    _lowerCAmelCase )






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






            def __getitem__(      self:    Any				,    _lowerCAmelCase:    Any ):
              return self.features[i]


if is_tf_available():
     import tensorflow as tf




     class       __lowerCAmelCase				:
            _a           =  42
            _a           =  -100






            def __init__(      self:    int				,    _lowerCAmelCase:    TokenClassificationTask				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    PreTrainedTokenizer				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[int] = None				,    _lowerCAmelCase:    Dict=False				,    _lowerCAmelCase:    Split = Split.train				,    ):
              lowercase			:Any							=  token_classification_task.read_examples_from_file(_lowerCAmelCase				,    _lowerCAmelCase )
              # TODO clean up all this to leverage built-in features of tokenizers
              lowercase			:Any							=  token_classification_task.convert_examples_to_features(
                  _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    cls_token_at_end=bool(model_type in ["xlnet"] )				,    cls_token=tokenizer.cls_token				,    cls_token_segment_id=2 if model_type in ["xlnet"] else 0				,    sep_token=tokenizer.sep_token				,    sep_token_extra=_lowerCAmelCase				,    pad_on_left=bool(tokenizer.padding_side == "left" )				,    pad_token=tokenizer.pad_token_id				,    pad_token_segment_id=tokenizer.pad_token_type_id				,    pad_token_label_id=self.pad_token_label_id				,    )

              def gen():
                for ex in self.features:
                  if ex.token_type_ids is None:
                    yield (
                        {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                        ex.label_ids,
                    )
                  else:
                    yield (
                        {
                            "input_ids": ex.input_ids,
                            "attention_mask": ex.attention_mask,
                            "token_type_ids": ex.token_type_ids,
                        },
                        ex.label_ids,
                    )

              if "token_type_ids" not in tokenizer.model_input_names:
                lowercase			:Optional[int]							=  tf.data.Dataset.from_generator(
                    _lowerCAmelCase				,    ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)				,    (
                        {"input_ids": tf.TensorShape([None] ), "attention_mask": tf.TensorShape([None] )},
                        tf.TensorShape([None] ),
                    )				,    )
              else:
                lowercase			:List[Any]							=  tf.data.Dataset.from_generator(
                    _lowerCAmelCase				,    ({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)				,    (
                        {
                            "input_ids": tf.TensorShape([None] ),
                            "attention_mask": tf.TensorShape([None] ),
                            "token_type_ids": tf.TensorShape([None] ),
                        },
                        tf.TensorShape([None] ),
                    )				,    )






            def     SCREAMING_SNAKE_CASE			(      self:    int ):
              lowercase			:Union[str, Any]							=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

              return self.dataset






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






            def __getitem__(      self:    Dict				,    _lowerCAmelCase:    Optional[Any] ):
              return self.features[i]









import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



