

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__	   = logging.get_logger(__name__)

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

lowerCAmelCase__	   = {
    """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__	   = {
    """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__	   = {
    """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__	   = {
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
lowerCAmelCase__	   = {
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
lowerCAmelCase__	   = {
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


lowerCAmelCase__	   = {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCAmelCase__	   = {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCAmelCase__	   = {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}

class 					a__						(					snake_case			):
			"""simple docstring"""







			__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 					a__						(					snake_case			):
			"""simple docstring"""







			__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

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

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


lowerCAmelCase__	   = 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(snake_case			)
class 					a__						:
			"""simple docstring"""







			def __call__(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase = False      ,      lowercase = False      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      **lowercase      ,      )  ->						BatchEncoding:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = 16      ,      lowercase = 64      ,      lowercase = 4      ,      )  ->						List[DPRSpanPrediction]:
							'''simple docstring'''






							A__        =			reader_input["input_ids"]
							A__    ,				A__    ,				A__        =			reader_output[:3]
							A__        =			len(lowercase       )
							A__        =			sorted(range(lowercase       )      ,      reverse=lowercase      ,      key=relevance_logits.__getitem__       )
							A__        =			[]
							for doc_id in sorted_docs:
											A__        =			list(input_ids[doc_id]       )
											# assuming question & title information is at the beginning of the sequence
											A__        =			sequence_ids.index(self.sep_token_id      ,      2       ) + 1  # second sep id
											if sequence_ids[-1] == self.pad_token_id:
															A__        =			sequence_ids.index(self.pad_token_id       )
											else:
															A__        =			len(lowercase       )

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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      )  ->						List[DPRSpanPrediction]:
							'''simple docstring'''






							A__        =			[]
							for start_index, start_score in enumerate(lowercase       ):
											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)       )
							A__        =			sorted(lowercase      ,      key=lambda lowercase       : x[1]      ,      reverse=lowercase       )
							A__        =			[]
							for (start_index, end_index), score in scores:
											assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
											A__        =			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(lowercase       ) == top_spans:
															break
							return chosen_span_intervals

@add_end_docstrings(snake_case			)
class 					a__						(					snake_case							,	snake_case			):
			"""simple docstring"""







			__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




import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__	   = """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__	   = direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase__	   = transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase__	   = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


lowerCAmelCase__	   = {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Dict:




				'''simple docstring'''





				A__        =			None

				# source code of `config_class`
				A__        =			inspect.getsource(SCREAMING_SNAKE_CASE_      )
				A__        =			_re_checkpoint.findall(SCREAMING_SNAKE_CASE_      )

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith("/"      ):
												A__        =			ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								A__        =			F'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												A__        =			ckpt_name
												break

				return checkpoint



def 					lowerCAmelCase__							(	)			->      List[str]:




				'''simple docstring'''





				A__        =			[]

				for config_class in list(CONFIG_MAPPING.values()      ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								A__        =			get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE_      )

								A__        =			config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(SCREAMING_SNAKE_CASE_      )

				if len(SCREAMING_SNAKE_CASE_      ) > 0:
								A__        =			"\n".join(sorted(SCREAMING_SNAKE_CASE_      )      )
								raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'      )


if __name__ == "__main__":
	check_config_docstrings_have_checkpoints()

