

import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

a__												=						[
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]

class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			,				_a			,				_a			,				_a = None			,				_a = None			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						None
												_a      :       Optional[Any]   =						os.path.abspath(os.path.join('''examples'''			,				'''by_feature'''			)			)
												_a      :       Optional[int]   =						os.path.abspath('''examples'''			)
												for item in os.listdir(_a			):
																	if item not in EXCLUDE_EXAMPLES:
																						_a      :       Any   =						os.path.join(_a			,				_a			)
																						if os.path.isfile(_a			) and ".py" in item_path:
																											with self.subTest(
																											    tested_script=_a			,				feature_script=_a			,				tested_section='''main()''' if parser_only else '''training_function()'''			,				):
																																_a      :       Optional[int]   =						compare_against_test(
																																    os.path.join(_a			,				_a			)			,				_a			,				_a			,				_a			)
																																_a      :       Union[str, Any]   =						'''\n'''.join(_a			)
																																if special_strings is not None:
																																					for string in special_strings:
																																										_a      :       Union[str, Any]   =						diff.replace(_a			,				''''''			)
																																self.assertEqual(_a			,				''''''			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												self.one_complete_example('''complete_nlp_example.py'''			,				_a			)
												self.one_complete_example('''complete_nlp_example.py'''			,				_a			)




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Optional[int]   =						os.path.abspath(os.path.join('''examples'''			,				'''cv_example.py'''			)			)
												_a      :       int   =						[
												    ''' ''' * 1_6 + '''{\n\n''',
												    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
												    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
												    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
												    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
												    ''' ''' * 1_6 + '''},\n\n''',
												    ''' ''' * 1_6 + '''step=epoch,\n''',
												    ''' ''' * 1_2,
												    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
												]
												self.one_complete_example('''complete_cv_example.py'''			,				_a			,				_a			,				_a			)
												self.one_complete_example('''complete_cv_example.py'''			,				_a			,				_a			,				_a			)



@mock.patch.dict(os.environ							,			{"TESTING_MOCKED_DATALOADERS": "1"}  )
class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       False


							@classmethod
							def 							__lowercase							(     cls			)       ->       List[Any]:
												super().setUpClass()
												_a      :       str   =						tempfile.mkdtemp()
												_a      :       str   =						os.path.join(cls._tmpdir			,				'''default_config.yml'''			)

												write_basic_config(save_location=cls.configPath			)
												_a      :       int   =						['''accelerate''', '''launch''', '''--config_file''', cls.configPath]


							@classmethod
							def 							__lowercase							(     cls			)       ->       Optional[int]:
												super().tearDownClass()
												shutil.rmtree(cls._tmpdir			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        """.split()
												run_command(self._launch_args + testargs			)
												self.assertTrue(os.path.exists(os.path.join(self.tmpdir			,				'''epoch_0'''			)			)			)


							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Union[str, Any]   =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        """.split()
												_a      :       List[str]   =						run_command(self._launch_args + testargs			)
												self.assertTrue(os.path.exists(os.path.join(self.tmpdir			,				'''step_2'''			)			)			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Dict   =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir			,				'epoch_0'			)}
        """.split()
												_a      :       str   =						run_command(self._launch_args + testargs			,				return_stdout=_a			)
												self.assertNotIn('''epoch 0:'''			,				_a			)
												self.assertIn('''epoch 1:'''			,				_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Optional[Any]   =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir			,				'step_2'			)}
        """.split()
												_a      :       Optional[int]   =						run_command(self._launch_args + testargs			,				return_stdout=_a			)
												if torch.cuda.is_available():
																	_a      :       List[Any]   =						torch.cuda.device_count()
												else:
																	_a      :       Tuple   =						1
												if num_processes > 1:
																	self.assertNotIn('''epoch 0:'''			,				_a			)
																	self.assertIn('''epoch 1:'''			,				_a			)
												else:
																	self.assertIn('''epoch 0:'''			,				_a			)
																	self.assertIn('''epoch 1:'''			,				_a			)


							@slow
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       List[str]   =						'''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
												with mock.patch.dict(os.environ			,				{'''TESTING_MOCKED_DATALOADERS''': '''0'''}			):
																	_a      :       Tuple   =						run_command(self._launch_args + testargs			,				return_stdout=_a			)
																	_a      :       int   =						re.findall('''({.+})'''			,				_a			)
																	_a      :       int   =						[r for r in results if '''accuracy''' in r][-1]
																	_a      :       Optional[Any]   =						ast.literal_eval(_a			)
																	self.assertGreaterEqual(results['''accuracy''']			,				0.75			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Optional[int]   =						['''examples/by_feature/multi_process_metrics.py''']
												run_command(self._launch_args + testargs			)


							@require_trackers
							@mock.patch.dict(os.environ			,				{'''WANDB_MODE''': '''offline'''}			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												with tempfile.TemporaryDirectory() as tmpdir:
																	_a      :       str   =						F"""
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            """.split()
																	run_command(self._launch_args + testargs			)
																	self.assertTrue(os.path.exists(os.path.join(_a			,				'''tracking'''			)			)			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						['''examples/by_feature/gradient_accumulation.py''']
												run_command(self._launch_args + testargs			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						['''examples/by_feature/local_sgd.py''']
												run_command(self._launch_args + testargs			)











import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowerCamelCase      :       int    =						logging.get_logger(__name__)

lowerCamelCase      :       Optional[int]    =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase      :       List[Any]    =						{
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
lowerCamelCase      :       int    =						{
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
lowerCamelCase      :       Optional[int]    =						{
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCamelCase      :       str    =						{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
lowerCamelCase      :       List[str]    =						{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
lowerCamelCase      :       Union[str, Any]    =						{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


lowerCamelCase      :       Dict    =						{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase      :       Union[str, Any]    =						{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase      :       Optional[Any]    =						{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				VOCAB_FILES_NAMES
      snake_case_	=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      snake_case_	=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_	=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      snake_case_	=				DPRContextEncoderTokenizer



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				VOCAB_FILES_NAMES
      snake_case_	=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      snake_case_	=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_	=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      snake_case_	=				DPRQuestionEncoderTokenizer

lowerCamelCase      :       Dict    =						collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowerCamelCase      :       int    =						collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCamelCase      :       Optional[int]    =						R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''



@add_start_docstrings(a_      )
class 				_UpperCamelCase      :


      def __call__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						BatchEncoding:
           if titles is None and texts is None:
                return super().__call__(
                    __UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           elif titles is None or texts is None:
                __lowerCAmelCase             =       titles if texts is None else texts
                return super().__call__(
                    __UpperCamelCase       ,    __UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           __lowerCAmelCase             =       titles if not isinstance(__UpperCamelCase       ,    __UpperCamelCase						) else [titles]
           __lowerCAmelCase             =       texts if not isinstance(__UpperCamelCase       ,    __UpperCamelCase						) else [texts]
           __lowerCAmelCase             =       len(__UpperCamelCase						)
           __lowerCAmelCase             =       questions if not isinstance(__UpperCamelCase       ,    __UpperCamelCase						) else [questions] * n_passages
           assert len(__UpperCamelCase						) == len(
               __UpperCamelCase						), F"""There should be as many titles than texts but got {len(__UpperCamelCase						)} titles and {len(__UpperCamelCase						)} texts."""
           __lowerCAmelCase             =       super().__call__(__UpperCamelCase       ,    __UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase						)["input_ids"]
           __lowerCAmelCase             =       super().__call__(__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase						)["input_ids"]
           __lowerCAmelCase             =       {
               "input_ids": [
                   (encoded_question_and_title + encoded_text)[:max_length]
                   if max_length is not None and truncation
                   else encoded_question_and_title + encoded_text
                   for encoded_question_and_title, encoded_text in zip(__UpperCamelCase       ,    __UpperCamelCase						)
               ]
           }
           if return_attention_mask is not False:
                __lowerCAmelCase             =       []
                for input_ids in encoded_inputs["input_ids"]:
                     attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
                __lowerCAmelCase             =       attention_mask
           return self.pad(__UpperCamelCase       ,    padding=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    return_tensors=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase = 1_6       ,    __UpperCamelCase = 6_4       ,    __UpperCamelCase = 4       ,    )->						List[DPRSpanPrediction]:
           __lowerCAmelCase             =       reader_input["input_ids"]
           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       reader_output[:3]
           __lowerCAmelCase             =       len(__UpperCamelCase						)
           __lowerCAmelCase             =       sorted(range(__UpperCamelCase						)       ,    reverse=__UpperCamelCase       ,    key=relevance_logits.__getitem__						)
           __lowerCAmelCase             =       []
           for doc_id in sorted_docs:
                __lowerCAmelCase             =       list(input_ids[doc_id]						)
                # assuming question & title information is at the beginning of the sequence
                __lowerCAmelCase             =       sequence_ids.index(self.sep_token_id       ,    2						) + 1  # second sep id
                if sequence_ids[-1] == self.pad_token_id:
                     __lowerCAmelCase             =       sequence_ids.index(self.pad_token_id						)
                else:
                     __lowerCAmelCase             =       len(__UpperCamelCase						)

                __lowerCAmelCase             =       self._get_best_spans(
                    start_logits=start_logits[doc_id][passage_offset:sequence_len]       ,    end_logits=end_logits[doc_id][passage_offset:sequence_len]       ,    max_answer_length=__UpperCamelCase       ,    top_spans=__UpperCamelCase       ,    )
                for start_index, end_index in best_spans:
                     start_index += passage_offset
                     end_index += passage_offset
                     nbest_spans_predictions.append(
                         DPRSpanPrediction(
                             span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]       ,    relevance_score=relevance_logits[doc_id]       ,    doc_id=__UpperCamelCase       ,    start_index=__UpperCamelCase       ,    end_index=__UpperCamelCase       ,    text=self.decode(sequence_ids[start_index : end_index + 1]						)       ,    )						)
                if len(__UpperCamelCase						) >= num_spans:
                     break
           return nbest_spans_predictions[:num_spans]


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    )->						List[DPRSpanPrediction]:
           __lowerCAmelCase             =       []
           for start_index, start_score in enumerate(__UpperCamelCase						):
                for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
                     scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
           __lowerCAmelCase             =       sorted(__UpperCamelCase       ,    key=lambda __UpperCamelCase						: x[1]       ,    reverse=__UpperCamelCase						)
           __lowerCAmelCase             =       []
           for (start_index, end_index), score in scores:
                assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
                __lowerCAmelCase             =       end_index - start_index + 1
                assert length <= max_answer_length, F"""Span is too long: {length} > {max_answer_length}"""
                if any(
                    start_index <= prev_start_index <= prev_end_index <= end_index
                    or prev_start_index <= start_index <= end_index <= prev_end_index
                    for (prev_start_index, prev_end_index) in chosen_span_intervals						):
                     continue
                chosen_span_intervals.append((start_index, end_index)						)

                if len(__UpperCamelCase						) == top_spans:
                     break
           return chosen_span_intervals



@add_end_docstrings(a_      )
class 				_UpperCamelCase      (a_				,		a_      ):
      snake_case_	=				VOCAB_FILES_NAMES
      snake_case_	=				READER_PRETRAINED_VOCAB_FILES_MAP
      snake_case_	=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_	=				READER_PRETRAINED_INIT_CONFIGURATION
      snake_case_	=				["""input_ids""", """attention_mask"""]
      snake_case_	=				DPRReaderTokenizer






