


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


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_					:      str
       lowercase_					:      List[str]
       lowercase_					:      Optional[List[str]]






@dataclass
class _lowerCamelCase:
       lowercase_					:      List[int]
       lowercase_					:      List[int]
       lowercase_					:      Optional[List[int]]       					=     None
       lowercase_					:      Optional[List[int]]       					=     None






class _lowerCamelCase(		_a  ):
       lowercase_					:      List[str]       					=     """train"""
       lowercase_					:      List[Any]       					=     """dev"""
       lowercase_					:      List[str]       					=     """test"""






class _lowerCamelCase:
       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase, lowerCamelCase)			->  List[InputExample]:




           """simple docstring"""

           raise NotImplementedError
       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase)			->  List[str]:




           """simple docstring"""

           raise NotImplementedError




       @staticmethod
       def      UpperCamelCase	(   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, )			->  List[InputFeatures]:




           """simple docstring"""

           _lowercase			:				Union[str, 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			:				Tuple 						=						[]
               for word, label in zip(example.words, example.labels):
                   _lowercase			:				Dict 						=						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			:				Optional[int] 						=						tokens[: (max_seq_length - special_tokens_count)]
                   _lowercase			:				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			:				List[str] 						=						[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			:				Any 						=						[pad_token_label_id] + label_ids
                   _lowercase			:				List[str] 						=						[cls_token_segment_id] + segment_ids

               _lowercase			:				Dict 						=						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			:				Optional[int] 						=						[1 if mask_padding_with_zero else 0] * len(lowerCamelCase)

               # Zero-pad up to the sequence length.
               _lowercase			:				Any 						=						max_seq_length - len(lowerCamelCase)
               if pad_on_left:
                   _lowercase			:				Dict 						=						([pad_token] * padding_length) + input_ids
                   _lowercase			:				Tuple 						=						([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   _lowercase			:				List[Any] 						=						([pad_token_segment_id] * padding_length) + segment_ids
                   _lowercase			:				Dict 						=						([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			:				Optional[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(		_a  ):
        lowercase_					:      List[InputFeatures]
        lowercase_					:      int       					=     nn.CrossEntropyLoss().ignore_index
        def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, lowerCamelCase=False, lowerCamelCase = Split.train, )			->  int:




            """simple docstring"""

            _lowercase			:				List[str] 						=						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			:				List[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			:				str 						=						torch.load(lowerCamelCase)
                else:
                    logger.info(F'''Creating features from dataset file at {data_dir}''')
                    _lowercase			:				Optional[int] 						=						token_classification_task.read_examples_from_file(lowerCamelCase, lowerCamelCase)
                    # TODO clean up all this to leverage built-in features of tokenizers
                    _lowercase			:				Optional[int] 						=						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)			->  int:




            """simple docstring"""

            return len(self.features)




        def __getitem__(   self, lowerCamelCase)			->  InputFeatures:




            """simple docstring"""

            return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class _lowerCamelCase:
        lowercase_					:      List[InputFeatures]
        lowercase_					:      int       					=     -1_00
        def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, lowerCamelCase=False, lowerCamelCase = Split.train, )			->  Optional[Any]:




            """simple docstring"""

            _lowercase			:				List[str] 						=						token_classification_task.read_examples_from_file(lowerCamelCase, lowerCamelCase)
            # TODO clean up all this to leverage built-in features of tokenizers
            _lowercase			:				int 						=						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[Any] 						=						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			:				Dict 						=						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      UpperCamelCase	(   self)			->  int:




            """simple docstring"""

            _lowercase			:				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, lowerCamelCase)			->  InputFeatures:




            """simple docstring"""

            return self.features[i]









import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
					from transformers.models.rag.configuration_rag import RagConfig
					from transformers.models.rag.tokenization_rag import RagTokenizer

@require_faiss
@require_torch
class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      tempfile.mkdtemp()
														UpperCamelCase__					  =      8

														# DPR tok
														UpperCamelCase__					  =      [
														    """[UNK]""",
														    """[CLS]""",
														    """[SEP]""",
														    """[PAD]""",
														    """[MASK]""",
														    """want""",
														    """##want""",
														    """##ed""",
														    """wa""",
														    """un""",
														    """runn""",
														    """##ing""",
														    """,""",
														    """low""",
														    """lowest""",
														]
														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""dpr_tokenizer"""	)
														os.makedirs(__lowerCAmelCase	,			exist_ok=__lowerCAmelCase	)
														UpperCamelCase__					  =      os.path.join(__lowerCAmelCase	,			DPR_VOCAB_FILES_NAMES["""vocab_file"""]	)
														with open(self.vocab_file	,			"""w"""	,			encoding="""utf-8"""	) as vocab_writer:
																					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]	)	)

														# BART tok
														UpperCamelCase__					  =      [
														    """l""",
														    """o""",
														    """w""",
														    """e""",
														    """r""",
														    """s""",
														    """t""",
														    """i""",
														    """d""",
														    """n""",
														    """\u0120""",
														    """\u0120l""",
														    """\u0120n""",
														    """\u0120lo""",
														    """\u0120low""",
														    """er""",
														    """\u0120lowest""",
														    """\u0120newer""",
														    """\u0120wider""",
														    """<unk>""",
														]
														UpperCamelCase__					  =      dict(zip(__lowerCAmelCase	,			range(len(__lowerCAmelCase	)	)	)	)
														UpperCamelCase__					  =      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														UpperCamelCase__					  =      {"""unk_token""": """<unk>"""}

														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""bart_tokenizer"""	)
														os.makedirs(__lowerCAmelCase	,			exist_ok=__lowerCAmelCase	)
														UpperCamelCase__					  =      os.path.join(__lowerCAmelCase	,			BART_VOCAB_FILES_NAMES["""vocab_file"""]	)
														UpperCamelCase__					  =      os.path.join(__lowerCAmelCase	,			BART_VOCAB_FILES_NAMES["""merges_file"""]	)
														with open(self.vocab_file	,			"""w"""	,			encoding="""utf-8"""	) as fp:
																					fp.write(json.dumps(__lowerCAmelCase	) + """\n"""	)
														with open(self.merges_file	,			"""w"""	,			encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(__lowerCAmelCase	)	)






							def 					_lowerCamelCase		(    self	):
														return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,			"""dpr_tokenizer"""	)	)






							def 					_lowerCamelCase		(    self	):
														return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,			"""bart_tokenizer"""	)	)






							def 					_lowerCamelCase		(    self	):
														shutil.rmtree(self.tmpdirname	)






							@require_tokenizers
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""rag_tokenizer"""	)
														UpperCamelCase__					  =      RagConfig(question_encoder=DPRConfig().to_dict()	,			generator=BartConfig().to_dict()	)
														UpperCamelCase__					  =      RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,			generator=self.get_bart_tokenizer()	)
														rag_config.save_pretrained(__lowerCAmelCase	)
														rag_tokenizer.save_pretrained(__lowerCAmelCase	)
														UpperCamelCase__					  =      RagTokenizer.from_pretrained(__lowerCAmelCase	,			config=__lowerCAmelCase	)
														self.assertIsInstance(new_rag_tokenizer.question_encoder	,			__lowerCAmelCase	)
														self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,			rag_tokenizer.question_encoder.get_vocab()	)
														self.assertIsInstance(new_rag_tokenizer.generator	,			__lowerCAmelCase	)
														self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,			rag_tokenizer.generator.get_vocab()	)






							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RagTokenizer.from_pretrained("""facebook/rag-token-nq"""	)
														UpperCamelCase__					  =      [
														    """who got the first nobel prize in physics""",
														    """when is the next deadpool movie being released""",
														    """which mode is used for short wave broadcast service""",
														    """who is the owner of reading football club""",
														    """when is the next scandal episode coming out""",
														    """when is the last time the philadelphia won the superbowl""",
														    """what is the most current adobe flash player version""",
														    """how many episodes are there in dragon ball z""",
														    """what is the first step in the evolution of the eye""",
														    """where is gall bladder situated in human body""",
														    """what is the main mineral in lithium batteries""",
														    """who is the president of usa right now""",
														    """where do the greasers live in the outsiders""",
														    """panda is a national animal of which country""",
														    """what is the name of manchester united stadium""",
														]
														UpperCamelCase__					  =      tokenizer(__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)







							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""	)
														UpperCamelCase__					  =      [
														    """who got the first nobel prize in physics""",
														    """when is the next deadpool movie being released""",
														    """which mode is used for short wave broadcast service""",
														    """who is the owner of reading football club""",
														    """when is the next scandal episode coming out""",
														    """when is the last time the philadelphia won the superbowl""",
														    """what is the most current adobe flash player version""",
														    """how many episodes are there in dragon ball z""",
														    """what is the first step in the evolution of the eye""",
														    """where is gall bladder situated in human body""",
														    """what is the main mineral in lithium batteries""",
														    """who is the president of usa right now""",
														    """where do the greasers live in the outsiders""",
														    """panda is a national animal of which country""",
														    """what is the name of manchester united stadium""",
														]
														UpperCamelCase__					  =      tokenizer(__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)




