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						:			Union[str, Any]     		=	logging.get_logger(__name__)

__UpperCamelCase						:			Union[str, Any]     		=	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase						:			Optional[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'''
        ),
    },
}
__UpperCamelCase						:			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'''
        ),
    },
}
__UpperCamelCase						:			Tuple     		=	{
    '''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						:			Any     		=	{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__UpperCamelCase						:			int     		=	{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
__UpperCamelCase						:			Tuple     		=	{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


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





class    lowercase__		(  a__):
						UpperCamelCase_	=						VOCAB_FILES_NAMES
						UpperCamelCase_	=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_	=						DPRContextEncoderTokenizer






class    lowercase__		(  a__):
						UpperCamelCase_	=						VOCAB_FILES_NAMES
						UpperCamelCase_	=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_	=						DPRQuestionEncoderTokenizer






__UpperCamelCase						:			Union[str, Any]     		=	collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__UpperCamelCase						:			List[Any]     		=	collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__UpperCamelCase						:			Tuple     		=	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    lowercase__		:


						def __call__(			self      :							List[Any]       ,						UpperCamelCase__      :							Optional[Any]       ,						UpperCamelCase__      :							Tuple = None       ,						UpperCamelCase__      :							Dict = None       ,						UpperCamelCase__      :							int = False       ,						UpperCamelCase__      :							Union[str, Any] = False       ,						UpperCamelCase__      :							str = None       ,						UpperCamelCase__      :							int = None       ,						UpperCamelCase__      :							Union[str, Any] = None       ,						**UpperCamelCase__      :							int       ,						):





												'''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:
																		SCREAMING_SNAKE_CASE				:       List[str]     							=				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       ,						)
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				titles if not isinstance(_UpperCAmelCase       ,						_UpperCAmelCase		) else [titles]
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				texts if not isinstance(_UpperCAmelCase       ,						_UpperCAmelCase		) else [texts]
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				len(_UpperCAmelCase		)
												SCREAMING_SNAKE_CASE				:       Any     							=				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."""
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				super().__call__(_UpperCAmelCase       ,						_UpperCAmelCase       ,						padding=_UpperCAmelCase       ,						truncation=_UpperCAmelCase		)['input_ids']
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				super().__call__(_UpperCAmelCase       ,						add_special_tokens=_UpperCAmelCase       ,						padding=_UpperCAmelCase       ,						truncation=_UpperCAmelCase		)['input_ids']
												SCREAMING_SNAKE_CASE				:       List[str]     							=				{
												    '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:
																		SCREAMING_SNAKE_CASE				:       Any     							=				[]
																		for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
																		SCREAMING_SNAKE_CASE				:       List[Any]     							=				attention_mask
												return self.pad(_UpperCAmelCase       ,						padding=_UpperCAmelCase       ,						max_length=_UpperCAmelCase       ,						return_tensors=_UpperCAmelCase		)


						def 			__A			(			self      :							int       ,						UpperCamelCase__      :							Dict       ,						UpperCamelCase__      :							Any       ,						UpperCamelCase__      :							int = 16       ,						UpperCamelCase__      :							str = 64       ,						UpperCamelCase__      :							Optional[int] = 4       ,						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       int     							=				reader_input['input_ids']
												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				reader_output[:3]
												SCREAMING_SNAKE_CASE				:       str     							=				len(_UpperCAmelCase		)
												SCREAMING_SNAKE_CASE				:       Any     							=				sorted(range(_UpperCAmelCase		)       ,						reverse=_UpperCAmelCase       ,						key=relevance_logits.__getitem__		)
												SCREAMING_SNAKE_CASE				:       List[DPRReaderOutput]     							=				[]
												for doc_id in sorted_docs:
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				list(input_ids[doc_id]		)
																		# assuming question & title information is at the beginning of the sequence
																		SCREAMING_SNAKE_CASE				:       Tuple     							=				sequence_ids.index(self.sep_token_id       ,						2		) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																								SCREAMING_SNAKE_CASE				:       int     							=				sequence_ids.index(self.pad_token_id		)
																		else:
																								SCREAMING_SNAKE_CASE				:       Optional[int]     							=				len(_UpperCAmelCase		)

																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				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 			__A			(			self      :							str       ,						UpperCamelCase__      :							Dict       ,						UpperCamelCase__      :							int       ,						UpperCamelCase__      :							Dict       ,						UpperCamelCase__      :							List[str]       ,						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       List[str]     							=				[]
												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)		)
												SCREAMING_SNAKE_CASE				:       Any     							=				sorted(_UpperCAmelCase       ,						key=lambda UpperCamelCase__		: x[1]       ,						reverse=_UpperCAmelCase		)
												SCREAMING_SNAKE_CASE				:       List[str]     							=				[]
												for (start_index, end_index), score in scores:
																		assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
																		SCREAMING_SNAKE_CASE				:       int     							=				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    lowercase__		(  a__      ,				a__):
						UpperCamelCase_	=						VOCAB_FILES_NAMES
						UpperCamelCase_	=						READER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	=						READER_PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_	=						["""input_ids""", """attention_mask"""]
						UpperCamelCase_	=						DPRReaderTokenizer












'''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,
)


lowercase__       : Any         =       '''hf-internal-testing/tiny-random-bert'''
lowercase__       : Optional[Any]         =       os.path.join(TRANSFORMERS_CACHE, '''models--hf-internal-testing--tiny-random-bert''')
lowercase__       : List[Any]         =       '''9b8c223d42b2188cb49d29af482996f9d0f3e5a6'''


class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

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

          # Using a specific revision to test the full commit hash.
          __A		:					List[Any]						=			cached_file(_UpperCAmelCase		,      _UpperCAmelCase		,      revision='9b8c223')
          self.assertEqual(_UpperCAmelCase		,      os.path.join(_UpperCAmelCase		,      'snapshots'		,      _UpperCAmelCase		,      _UpperCAmelCase))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          with self.assertRaisesRegex(_UpperCAmelCase		,      'is not a valid git identifier'):
               __A		:					Optional[int]						=			cached_file(_UpperCAmelCase		,      _UpperCAmelCase		,      revision='aaaa')

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

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

          __A		:					List[Any]						=			cached_file(_UpperCAmelCase		,      'conf'		,      _raise_exceptions_for_missing_entries=_UpperCAmelCase)
          self.assertIsNone(_UpperCAmelCase)

          __A		:					str						=			cached_file(_UpperCAmelCase		,      'conf'		,      local_files_only=_UpperCAmelCase		,      _raise_exceptions_for_missing_entries=_UpperCAmelCase)
          self.assertIsNone(_UpperCAmelCase)

          __A		:					List[str]						=			mock.Mock()
          __A		:					Dict						=			500
          __A		:					List[str]						=			{}
          __A		:					List[Any]						=			HTTPError
          __A		:					Optional[Any]						=			{}

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''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(_UpperCAmelCase		,      'is not a valid model identifier'):
               get_file_from_repo('bert-base-case'		,      _UpperCAmelCase)

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

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

               self.assertIsNone(get_file_from_repo(_UpperCAmelCase		,      'b.txt'))