
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


__magic_name__            =  logging.getLogger(__name__)




@dataclass
class 					_SCREAMING_SNAKE_CASE  :
      _A						:		str
      _A						:		List[str]
      _A						:		Optional[List[str]]





@dataclass
class 					_SCREAMING_SNAKE_CASE  :
      _A						:		List[int]
      _A						:		List[int]
      _A						:		Optional[List[int]]    	=     None
      _A						:		Optional[List[int]]    	=     None





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[str]    	=     'train'
      _A						:		Optional[Any]    	=     'dev'
      _A						:		Dict    	=     'test'





class 					_SCREAMING_SNAKE_CASE  :
      @staticmethod
      def A_			(			lowerCamelCase		,	lowerCamelCase ):
          raise NotImplementedError
      @staticmethod
      def A_			(			lowerCamelCase ):
          raise NotImplementedError



      @staticmethod
      def A_			(			lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase="[CLS]"		,	lowerCamelCase=1		,	lowerCamelCase="[SEP]"		,	lowerCamelCase=False		,	lowerCamelCase=False		,	lowerCamelCase=0		,	lowerCamelCase=0		,	lowerCamelCase=-1_00		,	lowerCamelCase=0		,	lowerCamelCase=True		,	):
          snake_case__						=				{label: i for i, label in enumerate(lowerCamelCase )}

          snake_case__						=				[]
          for ex_index, example in enumerate(lowerCamelCase ):
              if ex_index % 1_00_00 == 0:
                  logger.info("Writing example %d of %d"		,	lowerCamelCase		,	len(lowerCamelCase ) )

              snake_case__						=				[]
              snake_case__						=				[]
              for word, label in zip(example.words		,	example.labels ):
                  snake_case__						=				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.
              snake_case__						=				tokenizer.num_special_tokens_to_add()
              if len(lowerCamelCase ) > max_seq_length - special_tokens_count:
                  snake_case__						=				tokens[: (max_seq_length - special_tokens_count)]
                  snake_case__						=				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__						=				[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:
                  snake_case__						=				[cls_token] + tokens
                  snake_case__						=				[pad_token_label_id] + label_ids
                  snake_case__						=				[cls_token_segment_id] + segment_ids

              snake_case__						=				tokenizer.convert_tokens_to_ids(lowerCamelCase )

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

              # Zero-pad up to the sequence length.
              snake_case__						=				max_seq_length - len(lowerCamelCase )
              if pad_on_left:
                  snake_case__						=				([pad_token] * padding_length) + input_ids
                  snake_case__						=				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                  snake_case__						=				([pad_token_segment_id] * padding_length) + segment_ids
                  snake_case__						=				([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:
                  snake_case__						=				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 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
           _A						:		List[InputFeatures]
           _A						:		int    	=     nn.CrossEntropyLoss().ignore_index
           def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase=False		,	lowerCamelCase = Split.train		,	):
               # Load data features from cache or dataset file
               snake_case__						=				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.
               snake_case__						=				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}""" )
                       snake_case__						=				torch.load(lowerCamelCase )
                   else:
                       logger.info(F"""Creating features from dataset file at {data_dir}""" )
                       snake_case__						=				token_classification_task.read_examples_from_file(lowerCamelCase		,	lowerCamelCase )
                       # TODO clean up all this to leverage built-in features of tokenizers
                       snake_case__						=				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 ):
               return len(self.features )



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


if is_tf_available():
     import tensorflow as tf




     class 					_SCREAMING_SNAKE_CASE  :
           _A						:		List[InputFeatures]
           _A						:		int    	=     -1_00
           def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase=False		,	lowerCamelCase = Split.train		,	):
               snake_case__						=				token_classification_task.read_examples_from_file(lowerCamelCase		,	lowerCamelCase )
               # TODO clean up all this to leverage built-in features of tokenizers
               snake_case__						=				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:
                   snake_case__						=				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:
                   snake_case__						=				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 A_			(			self ):
               snake_case__						=				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

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



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





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[False] * len(__lowerCAmelCase							)
    snake_case__						=				[-1] * len(__lowerCAmelCase							)

    def dfs(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				True
        snake_case__						=				c
        for u in graph[v]:
            if not visited[u]:
                dfs(__lowerCAmelCase    ,		1 - c							)

    for i in range(len(__lowerCAmelCase							)							):
        if not visited[i]:
            dfs(__lowerCAmelCase    ,		0							)

    for i in range(len(__lowerCAmelCase							)							):
        for j in graph[i]:
            if color[i] == color[j]:
                return False

    return True


# Adjacency list of graph
__magic_name__            =  {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))



