





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


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_ 							=       {
    """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"""
        ),
    },
}
UpperCAmelCase_ 							=       {
    """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"""
        ),
    },
}
UpperCAmelCase_ 							=       {
    """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"""
        ),
    },
}

UpperCAmelCase_ 							=       {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
UpperCAmelCase_ 							=       {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
UpperCAmelCase_ 							=       {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


UpperCAmelCase_ 							=       {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
UpperCAmelCase_ 							=       {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
UpperCAmelCase_ 							=       {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_							=		DPRContextEncoderTokenizer





class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_							=		DPRQuestionEncoderTokenizer





UpperCAmelCase_ 							=       collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

UpperCAmelCase_ 							=       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


UpperCAmelCase_ 							=       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(_lowerCamelCase   )
class      UpperCamelCase_						:




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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 16			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 4			,  )						->							List[DPRSpanPrediction]:
										_snake_case													=			reader_input['input_ids']
										_snake_case							,       _snake_case							,       _snake_case													=			reader_output[:3]
										_snake_case													=			len(lowerCAmelCase_					)
										_snake_case													=			sorted(range(lowerCAmelCase_					)			,  reverse=lowerCAmelCase_			,  key=relevance_logits.__getitem__					)
										_snake_case													=			[]
										for doc_id in sorted_docs:
															_snake_case													=			list(input_ids[doc_id]					)
															# assuming question & title information is at the beginning of the sequence
															_snake_case													=			sequence_ids.index(self.sep_token_id			,  2					) + 1  # second sep id
															if sequence_ids[-1] == self.pad_token_id:
																				_snake_case													=			sequence_ids.index(self.pad_token_id					)
															else:
																				_snake_case													=			len(lowerCAmelCase_					)

															_snake_case													=			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=lowerCAmelCase_			,  top_spans=lowerCAmelCase_			,  )
															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=lowerCAmelCase_			,  start_index=lowerCAmelCase_			,  end_index=lowerCAmelCase_			,  text=self.decode(sequence_ids[start_index : end_index + 1]					)			,  )					)
															if len(lowerCAmelCase_					) >= num_spans:
																				break
										return nbest_spans_predictions[:num_spans]






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )						->							List[DPRSpanPrediction]:
										_snake_case													=			[]
										for start_index, start_score in enumerate(lowerCAmelCase_					):
															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)					)
										_snake_case													=			sorted(lowerCAmelCase_			,  key=lambda lowerCAmelCase_					: x[1]			,  reverse=lowerCAmelCase_					)
										_snake_case													=			[]
										for (start_index, end_index), score in scores:
															assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
															_snake_case													=			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(lowerCAmelCase_					) == top_spans:
																				break
										return chosen_span_intervals





@add_end_docstrings(_lowerCamelCase   )
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		READER_PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		READER_PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_							=		['''input_ids''', '''attention_mask''']
					lowerCAmelCase_							=		DPRReaderTokenizer










from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		'''gpt_neo'''
					lowerCAmelCase_							=		['''past_key_values''']
					lowerCAmelCase_							=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




					def __init__(						self			,  lowerCAmelCase_=5_0257			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=24			,  lowerCAmelCase_=[[["global", "local"], 12]]			,  lowerCAmelCase_=16			,  lowerCAmelCase_=None			,  lowerCAmelCase_=256			,  lowerCAmelCase_="gelu_new"			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.1			,  lowerCAmelCase_=1E-5			,  lowerCAmelCase_=0.02			,  lowerCAmelCase_=True			,  lowerCAmelCase_=5_0256			,  lowerCAmelCase_=5_0256			,  **lowerCAmelCase_			,  )						->							Tuple:
										_snake_case													=			vocab_size
										_snake_case													=			max_position_embeddings
										_snake_case													=			hidden_size
										_snake_case													=			num_layers
										_snake_case													=			num_heads
										_snake_case													=			intermediate_size
										_snake_case													=			window_size
										_snake_case													=			activation_function
										_snake_case													=			resid_dropout
										_snake_case													=			embed_dropout
										_snake_case													=			attention_dropout
										_snake_case													=			classifier_dropout
										_snake_case													=			layer_norm_epsilon
										_snake_case													=			initializer_range
										_snake_case													=			use_cache

										_snake_case													=			bos_token_id
										_snake_case													=			eos_token_id

										_snake_case													=			attention_types
										_snake_case													=			self.expand_attention_types_params(lowerCAmelCase_					)

										if len(self.attention_layers					) != self.num_layers:
															raise ValueError(
															    'Configuration for convolutional module is incorrect. '
															    'It is required that `len(config.attention_layers)` == `config.num_layers` '
															    F'''but is `len(config.attention_layers) = {len(self.attention_layers					)}`, '''
															    F'''`config.num_layers = {self.num_layers}`. '''
															    '`config.attention_layers` is prepared using `config.attention_types`. '
															    'Please verify the value of `config.attention_types` argument.'					)

										super().__init__(bos_token_id=lowerCAmelCase_			,  eos_token_id=lowerCAmelCase_			,  **lowerCAmelCase_					)






					@staticmethod
					def 		lowerCAmelCase     (						lowerCAmelCase_					)						->							Any:
										_snake_case													=			[]
										for item in attention_types:
															for _ in range(item[1]					):
																				attentions.extend(item[0]					)
										return attentions





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Any:



					'''simple docstring'''




					import torch

					_snake_case													=			input.size()
					_snake_case													=			len(UpperCamelCase__  )
					_snake_case													=			shape[dimension]

					_snake_case													=			torch.arange(0    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					_snake_case													=			torch.div(sizedim - size    ,      UpperCamelCase__    ,      rounding_mode='floor'  ) + 1
					_snake_case													=			torch.arange(UpperCamelCase__  ) + low_indices[:min_length][:, None]

					_snake_case													=			[slice(UpperCamelCase__  )] * rank
					_snake_case													=			indices
					_snake_case													=			input[s]

					_snake_case													=			list(range(0    ,      rank + 1  )  )
					perm.append(perm.pop(dimension + 1  )  )

					return sliced.permute(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict  )     ->	str:



					'''simple docstring'''




					import torch

					_snake_case													=			torch.arange(1    ,      UpperCamelCase__  )
					_snake_case													=			torch.remainder(UpperCamelCase__    ,      UpperCamelCase__  )
					_snake_case													=			remainders == 0
					_snake_case													=			candidates[divisor_indices]
					_snake_case													=			torch.max(UpperCamelCase__  )
					return largest_divisor, torch.div(UpperCamelCase__    ,      UpperCamelCase__    ,      rounding_mode='floor'  )




class      UpperCamelCase_						(					_lowerCamelCase   ):




					@property
					def 		lowerCAmelCase     (						self					)						->							Mapping[str, Mapping[int, str]]:
										_snake_case													=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
										if self.use_past:
															self.fill_with_past_key_values_(lowerCAmelCase_			,  direction='inputs'					)
															_snake_case													=			{0: 'batch', 1: 'past_sequence + sequence'}
										else:
															_snake_case													=			{0: 'batch', 1: 'sequence'}

										return common_inputs




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return self._config.num_heads




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = -1			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = None			,  )						->							Mapping[str, Any]:
										_snake_case													=			super(lowerCAmelCase_			,  self					).generate_dummy_inputs(
										    lowerCAmelCase_			,  batch_size=lowerCAmelCase_			,  seq_length=lowerCAmelCase_			,  is_pair=lowerCAmelCase_			,  framework=lowerCAmelCase_					)

										# We need to order the input in the way they appears in the forward()
										_snake_case													=			OrderedDict({'input_ids': common_inputs['input_ids']}					)

										# Need to add the past_keys
										if self.use_past:
															if not is_torch_available():
																				raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
															else:
																				import torch

																				_snake_case							,       _snake_case													=			common_inputs['input_ids'].shape
																				# Not using the same length for past_key_values
																				_snake_case													=			seqlen + 2
																				_snake_case													=			(
																				    batch,
																				    self.num_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // self.num_attention_heads,
																				)
																				_snake_case													=			[
																				    (torch.zeros(lowerCAmelCase_					), torch.zeros(lowerCAmelCase_					)) for _ in range(self.num_layers					)
																				]

										_snake_case													=			common_inputs['attention_mask']
										if self.use_past:
															_snake_case													=			ordered_inputs['attention_mask'].dtype
															_snake_case													=			torch.cat(
															    [ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase_			,  lowerCAmelCase_			,  dtype=lowerCAmelCase_					)]			,  dim=1					)

										return ordered_inputs






					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return 13



