


"""simple docstring"""



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


__snake_case         =					logging.getLogger(__name__)







@dataclass
class _lowerCAmelCase       :
   __UpperCAmelCase				: str
   __UpperCAmelCase				: List[str]
   __UpperCAmelCase				: Optional[List[str]]







@dataclass
class _lowerCAmelCase       :
   __UpperCAmelCase				: List[int]
   __UpperCAmelCase				: List[int]
   __UpperCAmelCase				: Optional[List[int]]													=						None
   __UpperCAmelCase				: Optional[List[int]]													=						None







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Dict													=						'''train'''
   __UpperCAmelCase				: int													=						'''dev'''
   __UpperCAmelCase				: Dict													=						'''test'''







class _lowerCAmelCase       :







   @staticmethod
   def 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__		)			->	List[InputExample]:

       '''simple docstring'''




       raise NotImplementedError







   @staticmethod
   def 				lowerCamelCase	(			UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       raise NotImplementedError







   @staticmethod
   def 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=False		,			UpperCamelCase__="[CLS]"		,			UpperCamelCase__=1		,			UpperCamelCase__="[SEP]"		,			UpperCamelCase__=False		,			UpperCamelCase__=False		,			UpperCamelCase__=0		,			UpperCamelCase__=0		,			UpperCamelCase__=-100		,			UpperCamelCase__=0		,			UpperCamelCase__=True		,			)			->	List[InputFeatures]:

       '''simple docstring'''




       snake_case					:   int         =					{label: i for i, label in enumerate(UpperCamelCase__		)}

       snake_case					:   List[str]         =					[]
       for ex_index, example in enumerate(UpperCamelCase__		):
           if ex_index % 1_0000 == 0:
               logger.info("Writing example %d of %d"		,			UpperCamelCase__		,			len(UpperCamelCase__		)		)

           snake_case					:   Optional[Any]         =					[]
           snake_case					:   int         =					[]
           for word, label in zip(example.words		,			example.labels		):
               snake_case					:   Optional[int]         =					tokenizer.tokenize(UpperCamelCase__		)

               # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
               if len(UpperCamelCase__		) > 0:
                   tokens.extend(UpperCamelCase__		)
                   # 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(UpperCamelCase__		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
           snake_case					:   List[str]         =					tokenizer.num_special_tokens_to_add()
           if len(UpperCamelCase__		) > max_seq_length - special_tokens_count:
               snake_case					:   int         =					tokens[: (max_seq_length - special_tokens_count)]
               snake_case					:   str         =					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]
           snake_case					:   List[Any]         =					[sequence_a_segment_id] * len(UpperCamelCase__		)

           if cls_token_at_end:
               tokens += [cls_token]
               label_ids += [pad_token_label_id]
               segment_ids += [cls_token_segment_id]
           else:
               snake_case					:   Dict         =					[cls_token] + tokens
               snake_case					:   str         =					[pad_token_label_id] + label_ids
               snake_case					:   Optional[int]         =					[cls_token_segment_id] + segment_ids

           snake_case					:   Union[str, Any]         =					tokenizer.convert_tokens_to_ids(UpperCamelCase__		)

           # The mask has 1 for real tokens and 0 for padding tokens. Only real
           # tokens are attended to.
           snake_case					:   str         =					[1 if mask_padding_with_zero else 0] * len(UpperCamelCase__		)

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

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

           if "token_type_ids" not in tokenizer.model_input_names:
               snake_case					:   Optional[Any]         =					None

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


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







  class _lowerCAmelCase       (   snake_case_			):
     __UpperCAmelCase				: List[InputFeatures]
     __UpperCAmelCase				: int													=						nn.CrossEntropyLoss().ignore_index







     def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			UpperCamelCase__=False		,			UpperCamelCase__ = Split.train		,			)			->	Optional[Any]:

         '''simple docstring'''




         snake_case					:   Optional[Any]         =					os.path.join(
             UpperCamelCase__		,			"cached_{}_{}_{}".format(mode.value		,			tokenizer.__class__.__name__		,			str(UpperCamelCase__		)		)		,			)

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         snake_case					:   Union[str, Any]         =					cached_features_file + ".lock"
         with FileLock(UpperCamelCase__		):
             if os.path.exists(UpperCamelCase__		) and not overwrite_cache:
                 logger.info(F'Loading features from cached file {cached_features_file}'		)
                 snake_case					:   int         =					torch.load(UpperCamelCase__		)
             else:
                 logger.info(F'Creating features from dataset file at {data_dir}'		)
                 snake_case					:   Optional[Any]         =					token_classification_task.read_examples_from_file(UpperCamelCase__		,			UpperCamelCase__		)
                 # TODO clean up all this to leverage built-in features of tokenizers
                 snake_case					:   Optional[Any]         =					token_classification_task.convert_examples_to_features(
                     UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			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=UpperCamelCase__		,			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		,			UpperCamelCase__		)







     def __len__(			self		)			->	str:

         '''simple docstring'''




         return len(self.features		)







     def __getitem__(			self		,			UpperCamelCase__		)			->	InputFeatures:

         '''simple docstring'''




         return self.features[i]


if is_tf_available():
  import tensorflow as tf







  class _lowerCAmelCase       :
     __UpperCAmelCase				: List[InputFeatures]
     __UpperCAmelCase				: int													=						-100







     def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = None		,			UpperCamelCase__=False		,			UpperCamelCase__ = Split.train		,			)			->	Any:

         '''simple docstring'''




         snake_case					:   Any         =					token_classification_task.read_examples_from_file(UpperCamelCase__		,			UpperCamelCase__		)
         # TODO clean up all this to leverage built-in features of tokenizers
         snake_case					:   Dict         =					token_classification_task.convert_examples_to_features(
             UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			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=UpperCamelCase__		,			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:
             snake_case					:   Union[str, Any]         =					tf.data.Dataset.from_generator(
                 UpperCamelCase__		,			({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)		,			(
                     {"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)},
                     tf.TensorShape([None]		),
                 )		,			)
         else:
             snake_case					:   Dict         =					tf.data.Dataset.from_generator(
                 UpperCamelCase__		,			({"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 				lowerCamelCase	(			self		)			->	List[str]:

         '''simple docstring'''




         snake_case					:   Optional[Any]         =					self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

         return self.dataset







     def __len__(			self		)			->	Any:

         '''simple docstring'''




         return len(self.features		)







     def __getitem__(			self		,			UpperCamelCase__		)			->	InputFeatures:

         '''simple docstring'''




         return self.features[i]









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						'''xmod'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=2		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=("en_XX",)		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		)

       snake_case					:   Optional[int]         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   int         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					intermediate_size
       snake_case					:   str         =					hidden_dropout_prob
       snake_case					:   List[str]         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Union[str, Any]         =					type_vocab_size
       snake_case					:   Union[str, Any]         =					initializer_range
       snake_case					:   str         =					layer_norm_eps
       snake_case					:   Dict         =					position_embedding_type
       snake_case					:   Any         =					use_cache
       snake_case					:   str         =					classifier_dropout
       snake_case					:   Any         =					pre_norm
       snake_case					:   Union[str, Any]         =					adapter_reduction_factor
       snake_case					:   Optional[int]         =					adapter_layer_norm
       snake_case					:   int         =					adapter_reuse_layer_norm
       snake_case					:   List[Any]         =					ln_before_adapter
       snake_case					:   Tuple         =					list(UpperCamelCase__		)
       snake_case					:   Tuple         =					default_language







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   str         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Tuple         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





