






'''simple docstring'''





import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


a_							=    '''hf-internal-testing/tiny-random-bert'''
a_							=    os.path.join(TRANSFORMERS_CACHE, '''models--hf-internal-testing--tiny-random-bert''')
a_							=    '''9b8c223d42b2188cb49d29af482996f9d0f3e5a6'''


class      lowercase__     (			unittest.TestCase	):

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						cached_file(__A		,    __A       )
							# Should have downloaded the file in here
							self.assertTrue(os.path.isdir(__A       )       )
							# Cache should contain at least those three subfolders:
							for subfolder in ["blobs", "refs", "snapshots"]:
													self.assertTrue(os.path.isdir(os.path.join(__A		,    __A       )       )       )
							with open(os.path.join(__A		,    "refs"		,    "main"       )       ) as f:
													lowerCAmelCase__       =						f.read()
							self.assertEqual(__A		,    os.path.join(__A		,    "snapshots"		,    __A		,    __A       )       )
							self.assertTrue(os.path.isfile(__A       )       )

							# File is cached at the same place the second time.
							lowerCAmelCase__       =						cached_file(__A		,    __A       )
							self.assertEqual(__A		,    __A       )

							# Using a specific revision to test the full commit hash.
							lowerCAmelCase__       =						cached_file(__A		,    __A		,    revision="9b8c223"       )
							self.assertEqual(__A		,    os.path.join(__A		,    "snapshots"		,    __A		,    __A       )       )

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							with self.assertRaisesRegex(__A		,    "is not a valid model identifier"       ):
													lowerCAmelCase__       =						cached_file("tiny-random-bert"		,    __A       )

							with self.assertRaisesRegex(__A		,    "is not a valid git identifier"       ):
													lowerCAmelCase__       =						cached_file(__A		,    __A		,    revision="aaaa"       )

							with self.assertRaisesRegex(__A		,    "does not appear to have a file named"       ):
													lowerCAmelCase__       =						cached_file(__A		,    "conf"       )

	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							with self.assertRaisesRegex(__A		,    "does not appear to have a file named"       ):
													lowerCAmelCase__       =						cached_file(__A		,    "conf"       )

							with open(os.path.join(__A		,    "refs"		,    "main"       )       ) as f:
													lowerCAmelCase__       =						f.read()
							self.assertTrue(os.path.isfile(os.path.join(__A		,    ".no_exist"		,    __A		,    "conf"       )       )       )

							lowerCAmelCase__       =						cached_file(__A		,    "conf"		,    _raise_exceptions_for_missing_entries=__A       )
							self.assertIsNone(__A       )

							lowerCAmelCase__       =						cached_file(__A		,    "conf"		,    local_files_only=__A		,    _raise_exceptions_for_missing_entries=__A       )
							self.assertIsNone(__A       )

							lowerCAmelCase__       =						mock.Mock()
							lowerCAmelCase__       =						500
							lowerCAmelCase__       =						{}
							lowerCAmelCase__       =						HTTPError
							lowerCAmelCase__       =						{}

							# Under the mock environment we get a 500 error when trying to reach the tokenizer.
							with mock.patch("requests.Session.request"		,    return_value=__A       ) as mock_head:
													lowerCAmelCase__       =						cached_file(__A		,    "conf"		,    _raise_exceptions_for_connection_errors=__A       )
													self.assertIsNone(__A       )
													# This check we did call the fake head request
													mock_head.assert_called()

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only"		,    __A       )       )
							self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"		,    __A       )       )
							self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"		,    __A       )       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							self.assertIsNone(get_file_from_repo("bert-base-cased"		,    "ahah.txt"       )       )

							# The function raises if the repository does not exist.
							with self.assertRaisesRegex(__A		,    "is not a valid model identifier"       ):
													get_file_from_repo("bert-base-case"		,    __A       )

							# The function raises if the revision does not exist.
							with self.assertRaisesRegex(__A		,    "is not a valid git identifier"       ):
													get_file_from_repo("bert-base-cased"		,    __A		,    revision="ahaha"       )

							lowerCAmelCase__       =						get_file_from_repo("bert-base-cased"		,    __A       )
							# The name is the cached name which is not very easy to test, so instead we load the content.
							lowerCAmelCase__       =						json.loads(open(__A		,    "r"       ).read()       )
							self.assertEqual(config["hidden_size"]		,    768       )


	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							with tempfile.TemporaryDirectory() as tmp_dir:
													lowerCAmelCase__       =						Path(__A       ) / "a.txt"
													filename.touch()
													self.assertEqual(get_file_from_repo(__A		,    "a.txt"       )		,    str(__A       )       )

													self.assertIsNone(get_file_from_repo(__A		,    "b.txt"       )       )














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 import BertTokenizer


a_							=    logging.get_logger(__name__)

a_							=    {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a_							=    {
    '''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'''
        ),
    },
}
a_							=    {
    '''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'''
        ),
    },
}
a_							=    {
    '''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'''
        ),
    },
}

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


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


class      lowercase__     (			_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	a_         =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class      lowercase__     (			_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	a_         =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

a_							=    collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


a_							=    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)

    Returns:
        `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(_UpperCAmelCase	)
class      lowercase__     :

	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = False		,    __UpperCAmelCase = False		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					BatchEncoding:







							'''simple docstring'''





							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
							if len(__UpperCAmelCase       ) != len(__UpperCAmelCase       ):
													raise ValueError(
													    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 = 16		,    __UpperCAmelCase = 64		,    __UpperCAmelCase = 4		,    )->					List[DPRSpanPrediction]:







							'''simple docstring'''





							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]:







							'''simple docstring'''





							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:
													if start_index > end_index:
																			raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"       )
													lowerCAmelCase__       =						end_index - start_index + 1
													if length > max_answer_length:
																			raise ValueError(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(_UpperCAmelCase	)
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =READER_PRETRAINED_VOCAB_FILES_MAP
	a_         =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =READER_PRETRAINED_INIT_CONFIGURATION
	a_         =["""input_ids""", """attention_mask"""]






