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


__SCREAMING_SNAKE_CASE						:   Dict         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   List[Any]         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE						:   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'
        ),
    },
}
__SCREAMING_SNAKE_CASE						:   Any         =			{
    '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'
        ),
    },
}
__SCREAMING_SNAKE_CASE						:   int         =			{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__SCREAMING_SNAKE_CASE						:   str         =			{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
__SCREAMING_SNAKE_CASE						:   List[Any]         =			{
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


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



class        lowercase_   (					__snake_case		):
							_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        lowercase_   (					__snake_case		):
							_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




__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__SCREAMING_SNAKE_CASE						:   Tuple         =			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '



@add_start_docstrings(__snake_case		)
class        lowercase_   :



							def __call__(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = False				,    lowercase_ = False				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    **lowercase_				,    ):
													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:
																			_snake_case						:					Dict    =   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_				,    )
													_snake_case						:					int    =   titles if not isinstance(lowercase_				,    lowercase_    ) else [titles]
													_snake_case						:					Dict    =   texts if not isinstance(lowercase_				,    lowercase_    ) else [texts]
													_snake_case						:					str    =   len(lowercase_    )
													_snake_case						:					int    =   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."""
													_snake_case						:					str    =   super().__call__(lowercase_				,    lowercase_				,    padding=lowercase_				,    truncation=lowercase_    )["input_ids"]
													_snake_case						:					List[Any]    =   super().__call__(lowercase_				,    add_special_tokens=lowercase_				,    padding=lowercase_				,    truncation=lowercase_    )["input_ids"]
													_snake_case						:					Any    =   {
													    "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:
																			_snake_case						:					int    =   []
																			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						:					Optional[Any]    =   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				,    ):
													_snake_case						:					Optional[Any]    =   reader_input["input_ids"]
													_snake_case    ,_snake_case    ,_snake_case						:					Optional[int]    =   reader_output[:3]
													_snake_case						:					int    =   len(lowercase_    )
													_snake_case						:					Optional[Any]    =   sorted(range(lowercase_    )				,    reverse=lowercase_				,    key=relevance_logits.__getitem__    )
													_snake_case						:					List[DPRReaderOutput]    =   []
													for doc_id in sorted_docs:
																			_snake_case						:					List[str]    =   list(input_ids[doc_id]    )
																			# assuming question & title information is at the beginning of the sequence
																			_snake_case						:					Optional[Any]    =   sequence_ids.index(self.sep_token_id				,    2    ) + 1  # second sep id
																			if sequence_ids[-1] == self.pad_token_id:
																									_snake_case						:					List[str]    =   sequence_ids.index(self.pad_token_id    )
																			else:
																									_snake_case						:					Dict    =   len(lowercase_    )

																			_snake_case						:					Dict    =   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_				,    ):
													_snake_case						:					Optional[int]    =   []
													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)    )
													_snake_case						:					List[Any]    =   sorted(lowercase_				,    key=lambda lowercase_    : x[1]				,    reverse=lowercase_    )
													_snake_case						:					int    =   []
													for (start_index, end_index), score in scores:
																			assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
																			_snake_case						:					Union[str, Any]    =   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        lowercase_   (					__snake_case						,			__snake_case		):
							_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
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)