

"""simple docstring"""


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


__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    snake_case     (    __snake_case  ):
							SCREAMING_SNAKE_CASE_       :				List[Any]       				=  VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_       :				List[Any]       				=  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_       :				Any       				=  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_       :				int       				=  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  DPRContextEncoderTokenizer





class    snake_case     (    __snake_case  ):
							SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_       :				List[str]       				=  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							SCREAMING_SNAKE_CASE_       :				Tuple       				=  DPRQuestionEncoderTokenizer


__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"\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    snake_case     :

							def __call__(     self							:    int					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[str] = None					,  UpperCamelCase__							:    Optional[str] = None					,  UpperCamelCase__							:    Union[bool, str] = False					,  UpperCamelCase__							:    Union[bool, str] = False					,  UpperCamelCase__							:    Optional[int] = None					,  UpperCamelCase__							:    Optional[Union[str, TensorType]] = None					,  UpperCamelCase__							:    Optional[bool] = None					,  **UpperCamelCase__							:    Dict					,  )->       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:	Tuple   				=      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:	Dict   				=      titles if not isinstance(UpperCamelCase__					,  UpperCamelCase__) else [titles]
									__lowerCAmelCase:	Optional[Any]   				=      texts if not isinstance(UpperCamelCase__					,  UpperCamelCase__) else [texts]
									__lowerCAmelCase:	Union[str, Any]   				=      len(UpperCamelCase__)
									__lowerCAmelCase:	List[str]   				=      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."
									__lowerCAmelCase:	Tuple   				=      super().__call__(UpperCamelCase__					,  UpperCamelCase__					,  padding=UpperCamelCase__					,  truncation=UpperCamelCase__)["input_ids"]
									__lowerCAmelCase:	Union[str, Any]   				=      super().__call__(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__					,  padding=UpperCamelCase__					,  truncation=UpperCamelCase__)["input_ids"]
									__lowerCAmelCase:	Optional[int]   				=      {
									    "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:	Tuple   				=      []
											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:	List[Any]   				=      attention_mask
									return self.pad(UpperCamelCase__					,  padding=UpperCamelCase__					,  max_length=UpperCamelCase__					,  return_tensors=UpperCamelCase__)

							def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    BatchEncoding					,  UpperCamelCase__							:    DPRReaderOutput					,  UpperCamelCase__							:    int = 1_6					,  UpperCamelCase__							:    int = 6_4					,  UpperCamelCase__							:    int = 4					,  )->       List[DPRSpanPrediction]:



									'''simple docstring'''


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

											__lowerCAmelCase:	Tuple   				=      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 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    List[int]					,  UpperCamelCase__							:    List[int]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  )->       List[DPRSpanPrediction]:



									'''simple docstring'''


									__lowerCAmelCase:	Tuple   				=      []
									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:	Tuple   				=      sorted(UpperCamelCase__					,  key=lambda UpperCamelCase__: x[1]					,  reverse=UpperCamelCase__)
									__lowerCAmelCase:	Tuple   				=      []
									for (start_index, end_index), score in scores:
											assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
											__lowerCAmelCase:	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(UpperCamelCase__) == top_spans:
													break
									return chosen_span_intervals





@add_end_docstrings(__snake_case  )
class    snake_case     (    __snake_case,							__snake_case  ):
							SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_       :				str       				=  READER_PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_       :				List[str]       				=  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_       :				int       				=  READER_PRETRAINED_INIT_CONFIGURATION
							SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  ["""input_ids""", """attention_mask"""]
							SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  DPRReaderTokenizer





"""simple docstring"""


import comet  # From: unbabel-comet
import torch

import datasets


__A						       =      datasets.logging.get_logger(__name__)

__A						       =      "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

__A						       =      "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

__A						       =      "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,							_KWARGS_DESCRIPTION  )
class    snake_case     (    datasets.Metric  ):

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  homepage="https://unbabel.github.io/COMET/html/index.html"					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
						        {
						            "sources": datasets.Value("string"					,  id="sequence"),
						            "predictions": datasets.Value("string"					,  id="sequence"),
						            "references": datasets.Value("string"					,  id="sequence"),
						        })					,  codebase_urls=["https://github.com/Unbabel/COMET"]					,  reference_urls=[
						        "https://github.com/Unbabel/COMET",
						        "https://www.aclweb.org/anthology/2020.emnlp-main.213/",
						        "http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
						    ]					,  )

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Any)->       Dict:



						'''simple docstring'''


						if self.config_name == "default":
								__lowerCAmelCase:	Union[str, Any]   				=      comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"))
						else:
								__lowerCAmelCase:	Tuple   				=      comet.load_from_checkpoint(comet.download_model(self.config_name))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    List[str]=False)->       str:



						'''simple docstring'''


						if gpus is None:
								__lowerCAmelCase:	Union[str, Any]   				=      1 if torch.cuda.is_available() else 0
						__lowerCAmelCase:	Dict   				=      {"src": sources, "mt": predictions, "ref": references}
						__lowerCAmelCase:	Union[str, Any]   				=      [dict(zip(UpperCamelCase__					,  UpperCamelCase__)) for t in zip(*data.values())]
						__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      self.scorer.predict(UpperCamelCase__					,  gpus=UpperCamelCase__					,  progress_bar=UpperCamelCase__)
						return {"mean_score": mean_score, "scores": scores}


