from pathlib import Path
from typing import List

import numpy as np

from immuneML.IO.ml_method.UtilIO import UtilIO
from immuneML.data_model.dataset.RepertoireDataset import RepertoireDataset
from immuneML.data_model.encoded_data.EncodedData import EncodedData
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.encodings.DatasetEncoder import DatasetEncoder
from immuneML.encodings.EncoderParams import EncoderParams
from immuneML.encodings.filtered_sequence_encoding.SequenceFilterHelper import SequenceFilterHelper
from immuneML.environment.Label import Label
from immuneML.pairwise_repertoire_comparison.ComparisonData import ComparisonData
from immuneML.util.EncoderHelper import EncoderHelper
from scripts.specification_util import update_docs_per_mapping


class SequenceAbundanceEncoder(DatasetEncoder):
    """
    This encoder represents the repertoires as vectors where:

    - the first element corresponds to the number of label-associated clonotypes
    - the second element is the total number of unique clonotypes

    To determine what clonotypes (with features defined by comparison_attributes) are label-associated
    based on a statistical test. The statistical test used is Fisher's exact test (one-sided).

    Reference: Emerson, Ryan O. et al.
    ‘Immunosequencing Identifies Signatures of Cytomegalovirus Exposure History and HLA-Mediated Effects on the T Cell Repertoire’.
    Nature Genetics 49, no. 5 (May 2017): 659–65. `doi.org/10.1038/ng.3822 <https://doi.org/10.1038/ng.3822>`_.

    Note: to use this encoder, it is necessary to explicitly define the positive class for the label when defining the label
    in the instruction. With positive class defined, it can then be determined which sequences are indicative of the positive class.
    For full example of using this encoder, see :ref:`Reproduction of the CMV status predictions study`.

    Arguments:

        comparison_attributes (list): The attributes to be considered to group receptors into clonotypes. Only the fields specified in
        comparison_attributes will be considered, all other fields are ignored. Valid comparison value can be any repertoire field name.

        p_value_threshold (float): The p value threshold to be used by the statistical test.

        sequence_batch_size (int): The number of sequences in a batch when comparing sequences across repertoires, typically 100s of thousands.
        This does not affect the results of the encoding, only the speed.

        repertoire_batch_size (int): How many repertoires will be loaded at once. This does not affect the result of the encoding, only the speed.
        This value is a trade-off between the number of repertoires that can fit the RAM at the time and loading time from disk.


    YAML specification:

    .. indent with spaces
    .. code-block:: yaml

        my_sa_encoding:
            SequenceAbundance:
                comparison_attributes:
                    - sequence_aas
                    - v_genes
                    - j_genes
                    - chains
                    - region_types
                p_value_threshold: 0.05
                sequence_batch_size: 100000
                repertoire_batch_size: 32

    """

    RELEVANT_SEQUENCE_ABUNDANCE = "relevant_sequence_abundance"
    TOTAL_SEQUENCE_ABUNDANCE = "total_sequence_abundance"

    def __init__(self, comparison_attributes, p_value_threshold: float, sequence_batch_size: int, repertoire_batch_size: int, name: str = None):
        self.comparison_attributes = comparison_attributes
        self.sequence_batch_size = sequence_batch_size
        self.name = name
        self.relevant_sequence_indices = None
        self.context = None
        self.p_value_threshold = p_value_threshold
        self.relevant_indices_path = None
        self.relevant_sequence_csv_path = None
        self.comparison_data = None
        self.repertoire_batch_size = repertoire_batch_size

    @staticmethod
    def build_object(dataset, **params):
        assert isinstance(dataset, RepertoireDataset), "FilteredSequenceEncoder: this encoding only works on repertoire datasets."
        return SequenceAbundanceEncoder(**params)

    def encode(self, dataset, params: EncoderParams):

        self._check_label(params)

        self.comparison_data = SequenceFilterHelper.build_comparison_data(dataset, self.context, self.comparison_attributes, params,
                                                                          self.sequence_batch_size)
        return self._encode_data(dataset, params)

    def _check_label(self, params: EncoderParams):

        labels = params.label_config.get_label_objects()

        assert len(labels) == 1, \
            "SequenceAbundanceEncoder: this encoding works only for single label."

        assert isinstance(labels[0], Label) and labels[0].positive_class is not None and labels[0].positive_class != "", \
            f"{SequenceAbundanceEncoder.__name__}: to use this encoder, in the label definition in the specification of the instruction, define " \
            f"the positive class for the label. Now it is set to '{labels[0].positive_class}'. See documentation for this encoder for more details."

    def _encode_data(self, dataset: RepertoireDataset, params: EncoderParams):

        label = params.label_config.get_labels_by_name()[0]

        examples = self._calculate_sequence_abundance(dataset, self.comparison_data, label, params)

        encoded_data = EncodedData(examples, dataset.get_metadata([label]) if params.encode_labels else None, dataset.get_repertoire_ids(),
                                   [SequenceAbundanceEncoder.RELEVANT_SEQUENCE_ABUNDANCE, SequenceAbundanceEncoder.TOTAL_SEQUENCE_ABUNDANCE],
                                   encoding=SequenceAbundanceEncoder.__name__, info={'relevant_sequence_path': self.relevant_sequence_csv_path})

        encoded_dataset = RepertoireDataset(labels=dataset.labels, encoded_data=encoded_data, repertoires=dataset.repertoires)

        return encoded_dataset

    def _calculate_sequence_abundance(self, dataset: RepertoireDataset, comparison_data: ComparisonData, label: str, params: EncoderParams):
        sequence_p_values_indices, indices_path, sequence_csv_path = SequenceFilterHelper.get_relevant_sequences(dataset=dataset, params=params,
                                                                                              comparison_data=comparison_data,
                                                                                              label=label, p_value_threshold=self.p_value_threshold,
                                                                                              comparison_attributes=self.comparison_attributes,
                                                                                              sequence_indices_path=self.relevant_indices_path)

        if self.relevant_indices_path is None:
            self.relevant_indices_path = indices_path

        if self.relevant_sequence_csv_path is None:
            self.relevant_sequence_csv_path = sequence_csv_path

        abundance_matrix = self._build_abundance_matrix(comparison_data, dataset.get_repertoire_ids(), sequence_p_values_indices)

        return abundance_matrix

    def _build_abundance_matrix(self, comparison_data, repertoire_ids, sequence_p_values_indices):
        abundance_matrix = np.zeros((len(repertoire_ids), 2))

        for index in range(0, len(repertoire_ids)+self.repertoire_batch_size, self.repertoire_batch_size):
            ind_start, ind_end = index, min(index+self.repertoire_batch_size, len(repertoire_ids))
            repertoire_vectors = comparison_data.get_repertoire_vectors(repertoire_ids[ind_start:ind_end])

            for rep_index in range(ind_start, ind_end):
                repertoire_vector = repertoire_vectors[repertoire_ids[rep_index]]
                relevant_sequence_abundance = np.sum(
                    repertoire_vector[np.logical_and(sequence_p_values_indices, repertoire_vector)])
                total_sequence_abundance = np.sum(repertoire_vector)
                abundance_matrix[rep_index] = [relevant_sequence_abundance, total_sequence_abundance]

        return abundance_matrix

    def set_context(self, context: dict):
        self.context = context
        return self

    def store(self, encoded_dataset, params: EncoderParams):
        EncoderHelper.store(encoded_dataset, params)

    @staticmethod
    def export_encoder(path: Path, encoder) -> Path:
        encoder_file = DatasetEncoder.store_encoder(encoder, path / "encoder.pickle")
        UtilIO.export_comparison_data(encoder.comparison_data, path)
        return encoder_file

    def get_additional_files(self) -> List[Path]:
        return [self.relevant_indices_path]

    @staticmethod
    def load_encoder(encoder_file: Path):
        encoder = DatasetEncoder.load_encoder(encoder_file)
        encoder.relevant_indices_path = DatasetEncoder.load_attribute(encoder, encoder_file, "relevant_indices_path")
        encoder.comparison_data = UtilIO.import_comparison_data(encoder_file.parent)
        return encoder

    @staticmethod
    def get_documentation():
        doc = str(SequenceAbundanceEncoder.__doc__)

        valid_field_values = str(Repertoire.FIELDS)[1:-1].replace("'", "`")
        mapping = {
            "Valid comparison value can be any repertoire field name.": f"Valid values are {valid_field_values}."
        }
        doc = update_docs_per_mapping(doc, mapping)
        return doc
