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


__lowerCamelCase	:					Union[str, Any]			  =					logging.getLogger(__name__)



@dataclass
class  A__       :
      _UpperCAmelCase     :str
      _UpperCAmelCase     :List[str]
      _UpperCAmelCase     :Optional[List[str]]



@dataclass
class  A__       :
      _UpperCAmelCase     :List[int]
      _UpperCAmelCase     :List[int]
      _UpperCAmelCase     :Optional[List[int]]						   =       None
      _UpperCAmelCase     :Optional[List[int]]						   =       None



class  A__       (	lowerCamelCase_      ):
      _UpperCAmelCase     :Dict						   =       '''train'''
      _UpperCAmelCase     :List[str]						   =       '''dev'''
      _UpperCAmelCase     :Dict						   =       '''test'''



class  A__       :





      @staticmethod
      def 	__UpperCamelCase(			A_	,							A_       ):
       '''simple docstring'''




       raise NotImplementedError





      @staticmethod
      def 	__UpperCamelCase(			A_       ):
       '''simple docstring'''




       raise NotImplementedError





      @staticmethod
      def 	__UpperCamelCase(			A_	,							A_	,							A_	,							A_	,							A_=False	,							A_="[CLS]"	,							A_=1	,							A_="[SEP]"	,							A_=False	,							A_=False	,							A_=0	,							A_=0	,							A_=-100	,							A_=0	,							A_=True	,							):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       {label: i for i, label in enumerate(__snake_case       )}

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

        UpperCamelCase     : Tuple	     =       []
        UpperCamelCase     : Tuple	     =       []
        for word, label in zip(example.words	,							example.labels       ):
         UpperCamelCase     : Tuple	     =       tokenizer.tokenize(__snake_case       )

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

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

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

        UpperCamelCase     : Dict	     =       tokenizer.convert_tokens_to_ids(__snake_case       )

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

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

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

        if "token_type_ids" not in tokenizer.model_input_names:
         UpperCamelCase     : Tuple	     =       None

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


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



    class  A__       (	lowerCamelCase_      ):
          _UpperCAmelCase     :List[InputFeatures]
          _UpperCAmelCase     :int						   =       nn.CrossEntropyLoss().ignore_index





          def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_ = None	,							A_=False	,							A_ = Split.train	,							):
           '''simple docstring'''




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

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





          def __len__(			self       ):
           '''simple docstring'''




           return len(self.features       )





          def __getitem__(			self	,							A_       ):
           '''simple docstring'''




           return self.features[i]


if is_tf_available():
    import tensorflow as tf



    class  A__       :
          _UpperCAmelCase     :List[InputFeatures]
          _UpperCAmelCase     :int						   =       -1_0_0





          def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_ = None	,							A_=False	,							A_ = Split.train	,							):
           '''simple docstring'''




           UpperCamelCase     : Union[str, Any]	     =       token_classification_task.read_examples_from_file(__snake_case	,							__snake_case       )
           # TODO clean up all this to leverage built-in features of tokenizers
           UpperCamelCase     : Tuple	     =       token_classification_task.convert_examples_to_features(
               __snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							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=__snake_case	,							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:
            UpperCamelCase     : int	     =       tf.data.Dataset.from_generator(
                __snake_case	,							({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,							(
                    {"input_ids": tf.TensorShape([None]       ), "attention_mask": tf.TensorShape([None]       )},
                    tf.TensorShape([None]       ),
                )	,							)
           else:
            UpperCamelCase     : str	     =       tf.data.Dataset.from_generator(
                __snake_case	,							({"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 	__UpperCamelCase(			self       ):
           '''simple docstring'''




           UpperCamelCase     : Tuple	     =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

           return self.dataset





          def __len__(			self       ):
           '''simple docstring'''




           return len(self.features       )





          def __getitem__(			self	,							A_       ):
           '''simple docstring'''




           return self.features[i]







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )						->			list[float]:
 UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       coefficient_matrix.shape
 UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       constant_matrix.shape

 if rowsa != colsa:
  UpperCamelCase     : List[Any]	     =       F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if colsa != 1:
  UpperCamelCase     : Any	     =       F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if rowsa != rowsa:
  UpperCamelCase     : Tuple	     =       (
      "Coefficient and constant matrices dimensions must be nxn and nx1 but "
      F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if len(_lowerCAmelCase					) != rowsa:
  UpperCamelCase     : Any	     =       (
      "Number of initial values must be equal to number of rows in coefficient "
      F"""matrix but received {len(_lowerCAmelCase					)} and {rowsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if iterations <= 0:
  raise ValueError("Iterations must be at least 1"					)

 UpperCamelCase     : NDArray[floataa]	     =       np.concatenate(
     (coefficient_matrix, constant_matrix)							,       axis=1					)

 UpperCamelCase     ,   UpperCamelCase     : str	     =       table.shape

 strictly_diagonally_dominant(_lowerCAmelCase					)

 # Iterates the whole matrix for given number of times
 for _ in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       []
  for row in range(_lowerCAmelCase					):
   UpperCamelCase     : Optional[int]	     =       0
   for col in range(_lowerCAmelCase					):
    if col == row:
     UpperCamelCase     : Union[str, Any]	     =       table[row][col]
    elif col == cols - 1:
     UpperCamelCase     : List[Any]	     =       table[row][col]
    else:
     temp += (-1) * table[row][col] * init_val[col]
   UpperCamelCase     : Dict	     =       (temp + val) / denom
   new_val.append(_lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       new_val

 return [float(_lowerCAmelCase					) for i in new_val]



def 		A_    (					_lowerCAmelCase					)						->			bool:
 UpperCamelCase     ,   UpperCamelCase     : Dict	     =       table.shape

 UpperCamelCase     : List[Any]	     =       True

 for i in range(0							,       _lowerCAmelCase					):
  UpperCamelCase     : Union[str, Any]	     =       0
  for j in range(0							,       cols - 1					):
   if i == j:
    continue
   else:
    total += table[i][j]

  if table[i][i] <= total:
   raise ValueError("Coefficient matrix is not strictly diagonally dominant"					)

 return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
    import doctest

    doctest.testmod()
