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evalkit_eagle/lib/python3.10/site-packages/openai-1.59.7.dist-info/entry_points.txt

@@ -0,0 +1,2 @@
+[console_scripts]
+openai = openai.cli:main

evalkit_eagle/lib/python3.10/site-packages/openai-1.59.7.dist-info/licenses/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
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evalkit_eagle/lib/python3.10/site-packages/regex/_regex.cpython-310-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29dd755c12af2aebb0e796b4bdd3878ddecf511ecd096f2a0e534c6ad6860f2c
+size 2549016

evalkit_eagle/lib/python3.10/site-packages/sacrebleu/__init__.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Copyright 2017--2018 Amazon.com, Inc. or its affiliates. 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. A copy of the License
+# is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file 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.
+
+__description__ = "Hassle-free computation of shareable, comparable, and reproducible BLEU, chrF, and TER scores"
+
+
+# Backward compatibility functions for old style API access (<= 1.4.10)
+from .compat import (
+    corpus_bleu,
+    corpus_chrf,
+    corpus_ter,
+    raw_corpus_bleu,
+    sentence_bleu,
+    sentence_chrf,
+    sentence_ter,
+)
+from .dataset import DATASETS
+from .metrics import BLEU, CHRF, TER
+from .metrics.helpers import extract_char_ngrams, extract_word_ngrams
+from .utils import (
+    SACREBLEU_DIR,
+    download_test_set,
+    get_available_testsets,
+    get_langpairs_for_testset,
+    get_reference_files,
+    get_source_file,
+    smart_open,
+)
+from .version import __version__
+
+__all__ = [
+    "smart_open",
+    "SACREBLEU_DIR",
+    "download_test_set",
+    "get_source_file",
+    "get_reference_files",
+    "get_available_testsets",
+    "get_langpairs_for_testset",
+    "extract_word_ngrams",
+    "extract_char_ngrams",
+    "DATASETS",
+    "BLEU",
+    "CHRF",
+    "TER",
+    "corpus_bleu",
+    "raw_corpus_bleu",
+    "sentence_bleu",
+    "corpus_chrf",
+    "sentence_chrf",
+    "corpus_ter",
+    "sentence_ter",
+    "__version__",
+]

evalkit_eagle/lib/python3.10/site-packages/sacrebleu/__main__.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Copyright 2017--2018 Amazon.com, Inc. or its affiliates. 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. A copy of the License
+# is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file 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.
+
+"""
+SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
+Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
+It also knows all the standard test sets and handles downloading, processing, and tokenization for you.
+
+See the [README.md] file for more information.
+"""
+from .sacrebleu import main
+
+if __name__ == '__main__':
+    main()

evalkit_eagle/lib/python3.10/site-packages/sacrebleu/compat.py

@@ -0,0 +1,205 @@
+from typing import Sequence, Optional
+
+from .metrics import BLEU, CHRF, TER, BLEUScore, CHRFScore, TERScore
+
+
+######################################################################
+# Backward compatibility functions for old style API access (< 1.4.11)
+######################################################################
+def corpus_bleu(hypotheses: Sequence[str],
+                references: Sequence[Sequence[str]],
+                smooth_method='exp',
+                smooth_value=None,
+                force=False,
+                lowercase=False,
+                tokenize=BLEU.TOKENIZER_DEFAULT,
+                use_effective_order=False) -> BLEUScore:
+    """Computes BLEU for a corpus against a single (or multiple) reference(s).
+    This is the main CLI entry point for computing BLEU between a system output
+    and a reference sentence.
+
+    :param hypotheses: A sequence of hypothesis strings.
+    :param references: A sequence of reference documents with document being
+        defined as a sequence of reference strings.
+    :param smooth_method: The smoothing method to use ('floor', 'add-k', 'exp' or 'none')
+    :param smooth_value: The smoothing value for `floor` and `add-k` methods. `None` falls back to default value.
+    :param force: Ignore data that looks already tokenized
+    :param lowercase: Lowercase the data
+    :param tokenize: The tokenizer to use
+    :param use_effective_order: Don't take into account n-gram orders without any match.
+    :return: a `BLEUScore` object
+    """
+    metric = BLEU(
+        lowercase=lowercase, force=force, tokenize=tokenize,
+        smooth_method=smooth_method, smooth_value=smooth_value,
+        effective_order=use_effective_order)
+
+    return metric.corpus_score(hypotheses, references)
+
+
+def raw_corpus_bleu(hypotheses: Sequence[str],
+                    references: Sequence[Sequence[str]],
+                    smooth_value: Optional[float] = BLEU.SMOOTH_DEFAULTS['floor']) -> BLEUScore:
+    """Computes BLEU for a corpus against a single (or multiple) reference(s).
+    This convenience function assumes a particular set of arguments i.e.
+    it disables tokenization and applies a `floor` smoothing with value `0.1`.
+
+    This convenience call does not apply any tokenization at all,
+    neither to the system output nor the reference. It just computes
+    BLEU on the "raw corpus" (hence the name).
+
+    :param hypotheses: A sequence of hypothesis strings.
+    :param references: A sequence of reference documents with document being
+        defined as a sequence of reference strings.
+    :param smooth_value: The smoothing value for `floor`. If not given, the default of 0.1 is used.
+    :return: Returns a `BLEUScore` object.
+
+    """
+    return corpus_bleu(
+        hypotheses, references, smooth_method='floor',
+        smooth_value=smooth_value, force=True, tokenize='none',
+        use_effective_order=True)
+
+
+def sentence_bleu(hypothesis: str,
+                  references: Sequence[str],
+                  smooth_method: str = 'exp',
+                  smooth_value: Optional[float] = None,
+                  lowercase: bool = False,
+                  tokenize=BLEU.TOKENIZER_DEFAULT,
+                  use_effective_order: bool = True) -> BLEUScore:
+    """
+    Computes BLEU for a single sentence against a single (or multiple) reference(s).
+
+    Disclaimer: Computing BLEU at the sentence level is not its intended use as
+    BLEU is a corpus-level metric.
+
+    :param hypothesis: A single hypothesis string.
+    :param references: A sequence of reference strings.
+    :param smooth_method: The smoothing method to use ('floor', 'add-k', 'exp' or 'none')
+    :param smooth_value: The smoothing value for `floor` and `add-k` methods. `None` falls back to default value.
+    :param lowercase: Lowercase the data
+    :param tokenize: The tokenizer to use
+    :param use_effective_order: Don't take into account n-gram orders without any match.
+    :return: Returns a `BLEUScore` object.
+    """
+    metric = BLEU(
+        lowercase=lowercase, tokenize=tokenize, force=False,
+        smooth_method=smooth_method, smooth_value=smooth_value,
+        effective_order=use_effective_order)
+
+    return metric.sentence_score(hypothesis, references)
+
+
+def corpus_chrf(hypotheses: Sequence[str],
+                references: Sequence[Sequence[str]],
+                char_order: int = CHRF.CHAR_ORDER,
+                word_order: int = CHRF.WORD_ORDER,
+                beta: int = CHRF.BETA,
+                remove_whitespace: bool = True,
+                eps_smoothing: bool = False) -> CHRFScore:
+    """
+    Computes chrF for a corpus against a single (or multiple) reference(s).
+    If `word_order` equals to 2, the metric is referred to as chrF++.
+
+    :param hypotheses: A sequence of hypothesis strings.
+    :param references: A sequence of reference documents with document being
+        defined as a sequence of reference strings.
+    :param char_order: Character n-gram order.
+    :param word_order: Word n-gram order. If equals to 2, the metric is referred to as chrF++.
+    :param beta: Determine the importance of recall w.r.t precision.
+    :param eps_smoothing: If `True`, applies epsilon smoothing similar
+    to reference chrF++.py, NLTK and Moses implementations. Otherwise,
+    it takes into account effective match order similar to sacreBLEU < 2.0.0.
+    :param remove_whitespace: If `True`, removes whitespaces prior to character n-gram extraction.
+    :return: A `CHRFScore` object.
+    """
+    metric = CHRF(
+        char_order=char_order,
+        word_order=word_order,
+        beta=beta,
+        whitespace=not remove_whitespace,
+        eps_smoothing=eps_smoothing)
+    return metric.corpus_score(hypotheses, references)
+
+
+def sentence_chrf(hypothesis: str,
+                  references: Sequence[str],
+                  char_order: int = CHRF.CHAR_ORDER,
+                  word_order: int = CHRF.WORD_ORDER,
+                  beta: int = CHRF.BETA,
+                  remove_whitespace: bool = True,
+                  eps_smoothing: bool = False) -> CHRFScore:
+    """
+    Computes chrF for a single sentence against a single (or multiple) reference(s).
+    If `word_order` equals to 2, the metric is referred to as chrF++.
+
+    :param hypothesis: A single hypothesis string.
+    :param references: A sequence of reference strings.
+    :param char_order: Character n-gram order.
+    :param word_order: Word n-gram order. If equals to 2, the metric is referred to as chrF++.
+    :param beta: Determine the importance of recall w.r.t precision.
+    :param eps_smoothing: If `True`, applies epsilon smoothing similar
+    to reference chrF++.py, NLTK and Moses implementations. Otherwise,
+    it takes into account effective match order similar to sacreBLEU < 2.0.0.
+    :param remove_whitespace: If `True`, removes whitespaces prior to character n-gram extraction.
+    :return: A `CHRFScore` object.
+    """
+    metric = CHRF(
+        char_order=char_order,
+        word_order=word_order,
+        beta=beta,
+        whitespace=not remove_whitespace,
+        eps_smoothing=eps_smoothing)
+    return metric.sentence_score(hypothesis, references)
+
+
+def corpus_ter(hypotheses: Sequence[str],
+               references: Sequence[Sequence[str]],
+               normalized: bool = False,
+               no_punct: bool = False,
+               asian_support: bool = False,
+               case_sensitive: bool = False) -> TERScore:
+    """
+    Computes TER for a corpus against a single (or multiple) reference(s).
+
+    :param hypotheses: A sequence of hypothesis strings.
+    :param references: A sequence of reference documents with document being
+        defined as a sequence of reference strings.
+    :param normalized: Enable character normalization.
+    :param no_punct: Remove punctuation.
+    :param asian_support: Enable special treatment of Asian characters.
+    :param case_sensitive: Enables case-sensitivity.
+    :return: A `TERScore` object.
+    """
+    metric = TER(
+        normalized=normalized,
+        no_punct=no_punct,
+        asian_support=asian_support,
+        case_sensitive=case_sensitive)
+    return metric.corpus_score(hypotheses, references)
+
+
+def sentence_ter(hypothesis: str,
+                 references: Sequence[str],
+                 normalized: bool = False,
+                 no_punct: bool = False,
+                 asian_support: bool = False,
+                 case_sensitive: bool = False) -> TERScore:
+    """
+    Computes TER for a single hypothesis against a single (or multiple) reference(s).
+
+    :param hypothesis: A single hypothesis string.
+    :param references: A sequence of reference strings.
+    :param normalized: Enable character normalization.
+    :param no_punct: Remove punctuation.
+    :param asian_support: Enable special treatment of Asian characters.
+    :param case_sensitive: Enable case-sensitivity.
+    :return: A `TERScore` object.
+    """
+    metric = TER(
+        normalized=normalized,
+        no_punct=no_punct,
+        asian_support=asian_support,
+        case_sensitive=case_sensitive)
+    return metric.sentence_score(hypothesis, references)

evalkit_eagle/lib/python3.10/site-packages/sacrebleu/sacrebleu.py

@@ -0,0 +1,576 @@
+#!/usr/bin/env python3
+
+# Copyright 2017--2018 Amazon.com, Inc. or its affiliates. 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. A copy of the License
+# is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file 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.
+
+"""
+SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
+Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
+It also knows all the standard test sets and handles downloading, processing, and tokenization for you.
+
+See the [README.md] file for more information.
+"""
+
+import io
+import os
+import sys
+import logging
+import pathlib
+import argparse
+from collections import defaultdict
+
+
+# Allows calling the script as a standalone utility
+# See: https://github.com/mjpost/sacrebleu/issues/86
+if __package__ is None and __name__ == '__main__':
+    parent = pathlib.Path(__file__).absolute().parents[1]
+    sys.path.insert(0, str(parent))
+    __package__ = 'sacrebleu'
+
+from .dataset import DATASETS
+from .metrics import METRICS
+from .utils import smart_open, filter_subset, get_langpairs_for_testset, get_available_testsets
+from .utils import print_test_set, print_subset_results, get_reference_files, download_test_set
+from .utils import args_to_dict, sanity_check_lengths, print_results_table, print_single_results
+from .utils import get_available_testsets_for_langpair, Color
+
+from . import __version__ as VERSION
+
+sacrelogger = logging.getLogger('sacrebleu')
+
+try:
+    # SIGPIPE is not available on Windows machines, throwing an exception.
+    from signal import SIGPIPE  # type: ignore
+
+    # If SIGPIPE is available, change behaviour to default instead of ignore.
+    from signal import signal, SIG_DFL
+    signal(SIGPIPE, SIG_DFL)
+except ImportError:
+    pass
+
+
+def parse_args():
+    arg_parser = argparse.ArgumentParser(
+        description='sacreBLEU: Hassle-free computation of shareable BLEU scores.\n'
+                    'Quick usage: score your detokenized output against WMT\'14 EN-DE:\n'
+                    '    cat output.detok.de | sacrebleu -t wmt14 -l en-de',
+        formatter_class=argparse.RawDescriptionHelpFormatter)
+
+    arg_parser.add_argument('--citation', '--cite', default=False, action='store_true',
+                            help='Dump the bibtex citation and quit.')
+    arg_parser.add_argument('--list', default=False, action='store_true',
+                            help='Print a list of all available test sets.')
+    arg_parser.add_argument('--test-set', '-t', type=str, default=None,
+                            help='The test set to use (see also --list) or a comma-separated list of test sets to be concatenated.')
+    arg_parser.add_argument('--language-pair', '-l', dest='langpair', default=None,
+                            help='Source-target language pair (2-char ISO639-1 codes).')
+    arg_parser.add_argument('--origlang', '-ol', dest='origlang', default=None,
+                            help='Use a subset of sentences with a given original language (2-char ISO639-1 codes), "non-" prefix means negation.')
+    arg_parser.add_argument('--subset', dest='subset', default=None,
+                            help='Use a subset of sentences whose document annotation matches a given regex (see SUBSETS in the source code).')
+    arg_parser.add_argument('--download', type=str, default=None,
+                            help='Download a test set and quit.')
+    arg_parser.add_argument('--echo', nargs="+", type=str, default=None,
+                            help='Output the source (src), reference (ref), or other available field (docid, ref:A, ref:1 for example) to STDOUT and quit. '
+                                 'You can get available fields with options `--list` and `-t`' 'For example: `sacrebleu -t wmt21 --list`. '
+                                 'If multiple fields are given, they are outputted with tsv format in the order they are given.'
+                                 'You can also use `--echo all` to output all available fields.')
+
+    # I/O related arguments
+    # Multiple input files can be provided for significance testing for example
+    arg_parser.add_argument('--input', '-i', type=str, nargs='*', default=None,
+                            help='Read input from file(s) instead of STDIN.')
+    arg_parser.add_argument('refs', nargs='*', default=[],
+                            help='Optional list of references. If given, it should preceed the -i/--input argument.')
+    arg_parser.add_argument('--num-refs', '-nr', type=int, default=1,
+                            help='Split the reference stream on tabs, and expect this many references. (Default: %(default)s)')
+    arg_parser.add_argument('--encoding', '-e', type=str, default='utf-8',
+                            help='Open text files with specified encoding (Default: %(default)s)')
+
+    # Metric selection
+    avail_metrics = [m.lower() for m in METRICS]
+    arg_parser.add_argument('--metrics', '-m', choices=avail_metrics, nargs='+', default=['bleu'],
+                            help='Space-delimited list of metrics to compute (Default: bleu)')
+    arg_parser.add_argument('--sentence-level', '-sl', action='store_true', help='Compute metric for each sentence.')
+
+    # BLEU-related arguments
+    # since sacreBLEU had only support for BLEU initially, the argument names
+    # are not prefixed with 'bleu' as in chrF arguments for example.
+    # Let's do that manually here through dest= options, as otherwise
+    # things will get quite hard to maintain when other metrics are added.
+    bleu_args = arg_parser.add_argument_group('BLEU related arguments')
+
+    bleu_args.add_argument('--smooth-method', '-s', choices=METRICS['BLEU'].SMOOTH_DEFAULTS.keys(), default='exp',
+                           dest='bleu_smooth_method',
+                           help='Smoothing method: exponential decay, floor (increment zero counts), add-k (increment num/denom by k for n>1), or none. (Default: %(default)s)')
+    bleu_args.add_argument('--smooth-value', '-sv', type=float, default=None,
+                           dest='bleu_smooth_value',
+                           help='The smoothing value. Only valid for floor and add-k. '
+                                f"(Defaults: floor: {METRICS['BLEU'].SMOOTH_DEFAULTS['floor']}, "
+                                f"add-k: {METRICS['BLEU'].SMOOTH_DEFAULTS['add-k']})")
+    bleu_args.add_argument('--tokenize', '-tok', choices=METRICS['BLEU'].TOKENIZERS, default=None,
+                           dest='bleu_tokenize',
+                           help='Tokenization method to use for BLEU. If not provided, defaults to `zh` for Chinese, '
+                                '`ja-mecab` for Japanese, `ko-mecab` for Korean and `13a` (mteval) otherwise.')
+    bleu_args.add_argument('--lowercase', '-lc', dest='bleu_lowercase', action='store_true', default=False,
+                           help='If True, enables case-insensitivity. (Default: %(default)s)')
+    bleu_args.add_argument('--force', default=False, action='store_true',
+                           dest='bleu_force', help='Insist that your tokenized input is actually detokenized.')
+
+    # ChrF-related arguments
+    chrf_args = arg_parser.add_argument_group('chrF related arguments')
+    chrf_args.add_argument('--chrf-char-order', '-cc', type=int, default=METRICS['CHRF'].CHAR_ORDER,
+                           help='Character n-gram order. (Default: %(default)s)')
+    chrf_args.add_argument('--chrf-word-order', '-cw', type=int, default=METRICS['CHRF'].WORD_ORDER,
+                           help='Word n-gram order (Default: %(default)s). If equals to 2, the metric is referred to as chrF++.')
+    chrf_args.add_argument('--chrf-beta', type=int, default=METRICS['CHRF'].BETA,
+                           help='Determine the importance of recall w.r.t precision. (Default: %(default)s)')
+    chrf_args.add_argument('--chrf-whitespace', action='store_true', default=False,
+                           help='Include whitespaces when extracting character n-grams. (Default: %(default)s)')
+    chrf_args.add_argument('--chrf-lowercase', action='store_true', default=False,
+                           help='Enable case-insensitivity. (Default: %(default)s)')
+    chrf_args.add_argument('--chrf-eps-smoothing', action='store_true', default=False,
+                           help='Enables epsilon smoothing similar to chrF++.py, NLTK and Moses; instead of effective order smoothing. (Default: %(default)s)')
+
+    # TER related arguments
+    ter_args = arg_parser.add_argument_group("TER related arguments (The defaults replicate TERCOM's behavior)")
+    ter_args.add_argument('--ter-case-sensitive', action='store_true',
+                          help='Enables case sensitivity. (Default: %(default)s)')
+    ter_args.add_argument('--ter-asian-support', action='store_true',
+                          help='Enables special treatment of Asian characters. (Default: %(default)s)')
+    ter_args.add_argument('--ter-no-punct', action='store_true',
+                          help='Removes punctuation. (Default: %(default)s)')
+    ter_args.add_argument('--ter-normalized', action='store_true',
+                          help='Applies basic normalization and tokenization. (Default: %(default)s)')
+
+    # Bootstrap resampling for confidence intervals
+    sign_args = arg_parser.add_argument_group('Confidence interval (CI) estimation for single-system evaluation')
+    sign_args.add_argument('--confidence', '-ci', action='store_true',
+                           help='Report confidence interval using bootstrap resampling.')
+    sign_args.add_argument('--confidence-n', '-cin', type=int, default=1000,
+                           help='Set the number of bootstrap resamples for CI estimation (Default: %(default)s).')
+
+    # Paired significance testing
+    pair_args = arg_parser.add_argument_group('Paired significance testing for multi-system evaluation')
+    pair_args_choice = pair_args.add_mutually_exclusive_group()
+
+    pair_args_choice.add_argument('--paired-ar', '-par', action='store_true',
+                                  help='Perform paired test using approximate randomization (AR). This option is '
+                                       'mutually exclusive with --paired-bs (Default: %(default)s).')
+    pair_args_choice.add_argument('--paired-bs', '-pbs', action='store_true',
+                                  help='Perform paired test using bootstrap resampling. This option is '
+                                       'mutually exclusive with --paired-ar (Default: %(default)s).')
+
+    pair_args.add_argument('--paired-ar-n', '-parn', type=int, default=10000,
+                           help='Number of trials for approximate randomization test (Default: %(default)s).')
+
+    pair_args.add_argument('--paired-bs-n', '-pbsn', type=int, default=1000,
+                           help='Number of bootstrap resamples for paired bootstrap resampling test (Default: %(default)s).')
+
+    pair_args.add_argument('--paired-jobs', '-j', type=int, default=1,
+                           help='If 0, launches as many workers as the number of systems. If > 0, sets the number of workers manually. '
+                                'This feature is currently not supported on Windows.')
+
+    # Reporting related arguments
+    report_args = arg_parser.add_argument_group('Reporting related arguments')
+    report_args.add_argument('--quiet', '-q', default=False, action='store_true',
+                             help='Suppress verbose messages.')
+    report_args.add_argument('--short', '-sh', default=False, action='store_true',
+                             help='Produce a shorter (less human readable) signature.')
+    report_args.add_argument('--score-only', '-b', default=False, action='store_true',
+                             help='Print only the computed score.')
+    report_args.add_argument('--width', '-w', type=int, default=1,
+                             help='Floating point width (Default: %(default)s).')
+    report_args.add_argument('--detail', '-d', default=False, action='store_true',
+                             help='Print detailed information (split test sets based on origlang).')
+    report_args.add_argument('--no-color', '-nc', action='store_true',
+                             help='Disable the occasional use of terminal colors.')
+
+    output_formats = ['json', 'text', 'latex']
+    report_args.add_argument('--format', '-f', default='json', choices=output_formats,
+                             help='Set the output format. `latex` is only valid for multi-system mode whereas '
+                                  '`json` and `text` apply to single-system mode only. This flag is overridden if the '
+                                  'SACREBLEU_FORMAT environment variable is set to one of the valid choices (Default: %(default)s).')
+
+    arg_parser.add_argument('--version', '-V', action='version', version='%(prog)s {}'.format(VERSION))
+
+    args = arg_parser.parse_args()
+
+    # Override the format from the environment, if any
+    if 'SACREBLEU_FORMAT' in os.environ:
+        _new_value = os.environ['SACREBLEU_FORMAT'].lower()
+        if _new_value in output_formats:
+            args.format = _new_value
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    # Is paired test requested?
+    paired_test_mode = args.paired_bs or args.paired_ar
+
+    # Explicitly set the encoding
+    sys.stdin = open(sys.stdin.fileno(), mode='r', encoding='utf-8', buffering=True, newline="\n")
+    sys.stdout = open(sys.stdout.fileno(), mode='w', encoding='utf-8', buffering=True)
+
+    if os.environ.get('NO_COLOR', False) or args.no_color:
+        Color.ENABLE_COLORS = False
+    else:
+        # These should come after all stdout manipulations otherwise cause
+        # issues esp. on Windows
+        import colorama
+        colorama.init()
+
+    if not args.quiet:
+        logging.basicConfig(level=logging.INFO, format='sacreBLEU: %(message)s')
+
+    if args.download:
+        download_test_set(args.download, args.langpair)
+        sys.exit(0)
+
+    if args.list:
+        if args.test_set:
+            for pair in [args.langpair] if args.langpair else get_langpairs_for_testset(args.test_set):
+                fields = DATASETS[args.test_set].fieldnames(pair)
+                print(f'{pair}: {", ".join(fields)}')
+        else:
+            if args.langpair:
+                print(f'The available test sets for {args.langpair} are:')
+                testsets = get_available_testsets_for_langpair(args.langpair)
+            else:
+                print('The available test sets are:')
+                testsets = get_available_testsets()
+            for testset in sorted(testsets):
+                desc = DATASETS[testset].description.strip()
+                print(f'{testset:<30}: {desc}')
+        sys.exit(0)
+
+    if args.sentence_level and len(args.metrics) > 1:
+        sacrelogger.error('Only one metric can be used in sentence-level mode.')
+        sys.exit(1)
+
+    if args.citation:
+        if not args.test_set:
+            sacrelogger.error('I need a test set (-t).')
+            sys.exit(1)
+        for test_set in args.test_set.split(','):
+            if 'citation' not in DATASETS[test_set]:
+                sacrelogger.error(f'No citation found for {test_set}')
+            else:
+                print(DATASETS[test_set].citation)
+        sys.exit(0)
+
+    if args.num_refs != 1 and (args.test_set is not None or len(args.refs) > 1):
+        sacrelogger.error('The --num-refs argument allows you to provide any number of tab-delimited references in a single file.')
+        sacrelogger.error('You can only use it with externally provided references, however (i.e., not with `-t`),')
+        sacrelogger.error('and you cannot then provide multiple reference files.')
+        sys.exit(1)
+
+    if args.test_set is not None:
+        for test_set in args.test_set.split(','):
+            if test_set not in DATASETS:
+                sacrelogger.error(f'Unknown test set {test_set!r}')
+                sacrelogger.error('Please run with --list to see the available test sets.')
+                sys.exit(1)
+
+    if args.test_set is None:
+        if len(args.refs) == 0:
+            sacrelogger.error('If manual references given, make sure to provide them '
+                              'before the -i/--input argument to avoid confusion.')
+            sacrelogger.error('Otherwise, I need a predefined test set (-t) from the following list:')
+            sacrelogger.error(get_available_testsets())
+            sys.exit(1)
+    elif len(args.refs) > 0:
+        sacrelogger.error('I need exactly one of (a) a predefined test set (-t) or (b) a list of references')
+        sys.exit(1)
+    elif args.langpair is None:
+        sacrelogger.error('I need a language pair (-l). Use --list to see available language pairs for this test set.')
+        sys.exit(1)
+    else:
+        for test_set in args.test_set.split(','):
+            langpairs = get_langpairs_for_testset(test_set)
+            if args.langpair not in langpairs:
+                sacrelogger.error(f'No such language pair {args.langpair!r}')
+                sacrelogger.error(f'Available language pairs for {test_set!r} are:')
+                for lp in langpairs:
+                    sacrelogger.error(f' > {lp}')
+                sys.exit(1)
+
+    if args.echo:
+        if args.langpair is None or args.test_set is None:
+            sacrelogger.warning("--echo requires a test set (--t) and a language pair (-l)")
+            sys.exit(1)
+        for test_set in args.test_set.split(','):
+            print_test_set(test_set, args.langpair, args.echo, args.origlang, args.subset)
+        sys.exit(0)
+
+    # Hack: inject target language info for BLEU, so that it can
+    # select the tokenizer based on it
+    if args.langpair:
+        args.bleu_trg_lang = args.langpair.split('-')[1]
+
+    if args.test_set is not None and args.bleu_tokenize == 'none':
+        sacrelogger.warning(
+            "You are turning off BLEU's internal tokenizer "
+            "presumably to supply your own tokenized files.")
+        sacrelogger.warning(
+            "Published numbers will not be comparable to other papers.")
+
+    # concat_ref_files is a list of list of reference filenames
+    # (concatenation happens if multiple test sets are given through -t)
+    # Example: [[testset1_refA, testset1_refB], [testset2_refA, testset2_refB]]
+    concat_ref_files = []
+    if args.test_set is None:
+        concat_ref_files.append(args.refs)
+    else:
+        # Multiple test sets can be given
+        for test_set in args.test_set.split(','):
+            ref_files = get_reference_files(test_set, args.langpair)
+            if len(ref_files) == 0:
+                sacrelogger.warning(
+                    f'No references found for test set {test_set}/{args.langpair}.')
+            concat_ref_files.append(ref_files)
+
+    #################
+    # Read references
+    #################
+    full_refs = [[] for x in range(max(len(concat_ref_files[0]), args.num_refs))]
+    for ref_files in concat_ref_files:
+        for refno, ref_file in enumerate(ref_files):
+            for lineno, line in enumerate(smart_open(ref_file, encoding=args.encoding), 1):
+                line = line.rstrip()
+                if args.num_refs == 1:
+                    full_refs[refno].append(line)
+                else:
+                    refs = line.split(sep='\t', maxsplit=args.num_refs - 1)
+                    # We are strict in fixed number of references through CLI
+                    # But the API supports having variable refs per each segment
+                    # by simply having '' or None's as dummy placeholders
+                    if len(refs) != args.num_refs:
+                        sacrelogger.error(f'FATAL: line {lineno}: expected {args.num_refs} fields, but found {len(refs)}.')
+                        sys.exit(17)
+                    for refno, ref in enumerate(refs):
+                        full_refs[refno].append(ref)
+
+    # Decide on the number of final references, override the argument
+    args.num_refs = len(full_refs)
+
+    # Read hypotheses
+    # Can't tokenize yet as each metric has its own way of tokenizing things
+    full_systems, sys_names = [], []
+
+    if args.input is None:
+        # Read from STDIN
+        inputfh = io.TextIOWrapper(sys.stdin.buffer, encoding=args.encoding)
+
+        # guess the number of systems by looking at the first line
+        fields = inputfh.readline().rstrip().split('\t')
+
+        # Set number of systems
+        num_sys = len(fields)
+
+        # place the first lines already
+        full_systems = [[s] for s in fields]
+
+        # Enumerate the systems
+        sys_names = [f'System {i + 1}' for i in range(num_sys)]
+
+        # Read the rest
+        for line in inputfh:
+            fields = line.rstrip().split('\t')
+            if len(fields) != num_sys:
+                sacrelogger.error('FATAL: the number of tab-delimited fields in the input stream differ across lines.')
+                sys.exit(17)
+            # Place systems into the list
+            for sys_idx, sent in enumerate(fields):
+                full_systems[sys_idx].append(sent.rstrip())
+    else:
+        # Separate files are given for each system output
+        # Ex: --input smt.txt nmt.txt
+        for fname in args.input:
+            sys_name = fname
+
+            if sys_name in sys_names:
+                if paired_test_mode and sys_name == sys_names[0]:
+                    # We skip loading a system, if it was already the baseline
+                    sacrelogger.info(f'Ignoring {sys_name!r} as it was also given as the baseline.')
+                    continue
+                else:
+                    # To avoid ambiguities, we fail if two systems have same names
+                    sacrelogger.error(f"{sys_name!r} already used to name a system.")
+                    sacrelogger.error("Make sure to have a different basename for each system.")
+                    sys.exit(1)
+
+            # Read the system
+            lines = []
+            for line in smart_open(fname, encoding=args.encoding):
+                lines.append(line.rstrip())
+            full_systems.append(lines)
+            sys_names.append(sys_name)
+
+        # Set final number of systems
+        num_sys = len(sys_names)
+
+    # Add baseline prefix to the first system for clarity
+    if paired_test_mode:
+        if args.input is None:
+            # STDIN mode, no explicit system names
+            sys_names = ['Baseline'] + [f'System {i + 1}' for i in range(num_sys - 1)]
+        else:
+            # --input mode, we have names for the systems, just change the 1st one
+            sys_names[0] = f'Baseline: {sys_names[0]}'
+
+    if args.sentence_level:
+        if num_sys > 1:
+            sacrelogger.error('Only one system can be evaluated in sentence-level mode.')
+            sys.exit(1)
+        if args.confidence or paired_test_mode:
+            sacrelogger.error('Statistical tests are unavailable in sentence-level mode.')
+            sys.exit(1)
+
+        # >=2.0.0: effective_order is now part of BLEU class. For sentence-BLEU
+        # we now need to explicitly enable it without user's intervention
+        # for backward compatibility.
+        args.bleu_effective_order = True
+
+    if paired_test_mode and num_sys == 1:
+        sacrelogger.error('Paired tests require multiple input systems given to --input (-i).')
+        sys.exit(1)
+
+    if num_sys > 1 and args.confidence:
+        sacrelogger.error('Use paired tests (--paired) for multiple systems.')
+        sys.exit(1)
+
+    # Filter subsets if requested
+    outputs = filter_subset(
+        [*full_systems, *full_refs], args.test_set, args.langpair,
+        args.origlang, args.subset)
+
+    # Unpack systems & references back
+    systems, refs = outputs[:num_sys], outputs[num_sys:]
+
+    # Perform some sanity checks
+    for system in systems:
+        if len(system) == 0:
+            message = f'Test set {args.test_set!r} contains no sentence'
+            if args.origlang is not None or args.subset is not None:
+                message += ' with'
+                if args.origlang:
+                    message += f' origlang={args.origlang}'
+                if args.subset:
+                    message += f' subset={args.subset}' + args.subset
+            sacrelogger.error(message)
+            sys.exit(1)
+
+        # Check lengths
+        sanity_check_lengths(system, refs, test_set=args.test_set)
+
+    # Create the metrics
+    metrics = {}
+    for name in args.metrics:
+        # Each metric's specific arguments are prefixed with `metricname_`
+        # for grouping. Filter accordingly and strip the prefixes prior to
+        # metric object construction.
+        metric_args = args_to_dict(args, name.lower(), strip_prefix=True)
+
+        # This will cache reference stats for faster re-computation if required
+        metric_args['references'] = refs
+
+        # Make it uppercase for the rest of the code
+        name = name.upper()
+        metrics[name] = METRICS[name](**metric_args)
+
+    # Handle sentence level and quit
+    if args.sentence_level:
+        # one metric and one system in use for sentence-level
+        metric, system = list(metrics.values())[0], systems[0]
+
+        for hypothesis, *references in zip(system, *refs):
+            score = metric.sentence_score(hypothesis, references)
+            sig = metric.get_signature().format(args.short)
+            print(score.format(args.width, args.score_only, sig))
+
+        sys.exit(0)
+
+    if args.detail and args.format == 'json':
+        # The translationese info will interfere with JSON output, disable
+        args.format = 'text'
+
+    ##############################
+    # Corpus level evaluation mode
+    ##############################
+    if num_sys == 1:
+        # Single system evaluation mode
+        results = []
+        for name in sorted(metrics):
+            # compute the score
+            score = metrics[name].corpus_score(
+                system, references=None,
+                n_bootstrap=args.confidence_n if args.confidence else 1)
+            # get the signature
+            sig = metrics[name].get_signature().format(
+                args.short if args.format != 'json' else False)
+            results.append(
+                score.format(args.width, args.score_only, sig, args.format == 'json'))
+
+        print_single_results(results, args)
+
+        # Prints detailed information for translationese effect experiments
+        if args.detail:
+            print_subset_results(metrics, full_systems[0], full_refs, args)
+    else:
+        # Multi-system evaluation mode
+        named_systems = [(sys_names[i], systems[i]) for i in range(num_sys)]
+        sacrelogger.info(f'Found {num_sys} systems.')
+
+        if not paired_test_mode:
+            # Bootstrap resampling or the usual single score computation mode
+            sigs = {}
+            scores = defaultdict(list)
+            scores['System'] = sys_names
+
+            for sys_name, system in named_systems:
+                for name in sorted(metrics):
+                    score = metrics[name].corpus_score(system, references=None)
+                    sigs[score.name] = metrics[name].get_signature().format(args.short)
+                    scores[score.name].append(score.format(args.width, True))
+
+        else:
+            # Paired significance testing mode
+            from .significance import PairedTest
+
+            # Set params
+            test_type = 'bs' if args.paired_bs else 'ar'
+            n_samples = args.paired_bs_n if args.paired_bs else args.paired_ar_n
+
+            ps = PairedTest(named_systems, metrics, references=None,
+                            test_type=test_type, n_samples=n_samples,
+                            n_jobs=args.paired_jobs)
+
+            # Set back the number of trials
+            args.paired_n = ps.n_samples
+
+            # Run the test
+            sigs, scores = ps()
+
+            # Get signature strings
+            sigs = {k: v.format(args.short) for k, v in sigs.items()}
+
+        # Dump the results
+        print_results_table(scores, sigs, args)
+
+
+if __name__ == '__main__':
+    main()

evalkit_eagle/lib/python3.10/site-packages/sacrebleu/significance.py

@@ -0,0 +1,435 @@
+import os
+import logging
+import multiprocessing as mp
+from typing import Sequence, Dict, Optional, Tuple, List, Union, Any, Mapping
+
+import numpy as np
+
+from .metrics.base import Metric, Score, Signature
+
+IS_WINDOWS = os.name == 'nt'
+
+
+sacrelogger = logging.getLogger('sacrebleu')
+
+
+class Result:
+    """A container to represent results from a particular statistical
+    significance test.
+    :param score: The floating point score for the system at hand.
+    :param p_value: If exists, represents the p-value when the system at
+    hand is compared to a baseline using a paired test.
+    :param mean: When paired bootstrap test is applied, this represents
+    the true mean score estimated from bootstrap resamples of the system.
+    :param ci: When paired bootstrap test is applied, this represents
+    the 95% confidence interval around the true mean score `sys_mean`.
+    """
+    def __init__(self, score: float, p_value: Optional[float] = None,
+                 mean: Optional[float] = None, ci: Optional[float] = None):
+        self.score = score
+        self.p_value = p_value
+        self.mean = mean
+        self.ci = ci
+
+    def __repr__(self):
+        return ','.join([f'{k}={str(v)}' for k, v in self.__dict__.items()])
+
+
+def estimate_ci(scores: np.ndarray) -> Tuple[float, float]:
+    """Takes a list of scores and returns mean and 95% confidence
+    interval around the mean.
+
+    :param scores: A list of floating point scores.
+    :return: A tuple of mean and the 95% CI.
+    """
+    # Sort the scores
+    scores = np.sort(scores)
+    n = len(scores)
+
+    # Get CI bounds (95%, i.e. 1/40 from left)
+    lower_idx = n // 40
+    upper_idx = n - lower_idx - 1
+    lower, upper = scores[lower_idx], scores[upper_idx]
+    ci = 0.5 * (upper - lower)
+    return (scores.mean(), ci)
+
+
+def _bootstrap_resample(stats: List[List[Union[int, float]]],
+                        metric: Metric, n_samples: int = 1000) -> Tuple[str, List[Score]]:
+    """Performs bootstrap resampling for a single system to estimate
+    a confidence interval around the true mean.
+    :param stats: A list of statistics extracted from the system's hypotheses.
+    :param metric: The `Metric` instance to be used for score computation.
+    :n_samples: Number of bootstrap resamples to use.
+
+    :return: A tuple of the seed choice as string and the list of `Score`
+    instances for all bootstrap resamples.
+    """
+
+    # Set numpy RNG's seed
+    # If  given -> Fix to the given value
+    # If given but =='[Nn]one', don't fix the seed i.e. pull entropy from OS
+    seed = os.environ.get('SACREBLEU_SEED', '12345')
+    _seed = None if seed.lower() == 'none' else int(seed)
+    rng = np.random.default_rng(_seed)
+
+    # The indices that'll produce all bootstrap resamples at once
+    idxs = rng.choice(len(stats), size=(n_samples, len(stats)), replace=True)
+
+    # convert to numpy array. float32 is more efficient
+    stats_np = np.array(stats, dtype='float32')
+
+    # recompute scores for all resamples
+    scores = [
+        metric._compute_score_from_stats(_s.sum(0)) for _s in stats_np[idxs]]
+
+    return str(seed).lower(), scores
+
+
+def _compute_p_value(stats: np.ndarray, real_difference: float) -> float:
+    """Computes the p-value given the sample statistics and the real statistic.
+    :param stats: A numpy array with the sample statistics.
+    :real_difference: The real statistic.
+    :return: The p-value.
+    """
+    # Taken from: significance/StratifiedApproximateRandomizationTest.java
+    # https://github.com/jhclark/multeval.git
+
+    # "the != is important. if we want to score the same system against itself
+    # having a zero difference should not be attributed to chance."
+
+    c = np.sum(stats > real_difference).item()
+
+    # "+1 applies here, though it only matters for small numbers of shufflings,
+    # which we typically never do. it's necessary to ensure the probability of
+    # falsely rejecting the null hypothesis is no greater than the rejection
+    # level of the test (see william and morgan on significance tests)
+    p = (c + 1) / (len(stats) + 1)
+
+    return p
+
+
+def _paired_ar_test(baseline_info: Dict[str, Tuple[np.ndarray, Result]],
+                    sys_name: str,
+                    hypotheses: Sequence[str],
+                    references: Optional[Sequence[Sequence[str]]],
+                    metrics: Dict[str, Metric],
+                    n_samples: int = 10000,
+                    n_ar_confidence: int = -1,
+                    seed: Optional[int] = None) -> Tuple[str, Dict[str, Result]]:
+    """Paired two-sided approximate randomization (AR) test for MT evaluation.
+
+    :param baseline_info: A dictionary with `Metric` instances as the keys,
+    that contains sufficient statistics and a `Result` instance for the baseline system.
+    :param sys_name: The name of the system to be evaluated.
+    :param hypotheses: A sequence of string hypotheses for the system.
+    :param references: A sequence of reference documents with document being
+    defined as a sequence of reference strings. If `None`, references
+    will be used through each metric's internal cache.
+    :param metrics: A dictionary of `Metric` instances that will be computed
+    for each system.
+    :param n_samples: The number of AR trials.
+    :param n_ar_confidence: The number of bootstrap resamples to use for
+    confidence estimation. A value of -1 disables confidence estimation.
+    :param seed: The seed value for the RNG. If `None`, the RNG will not be
+    fixed to a particular seed.
+
+    :return: A tuple with first element being the system name and the second
+    being a `Result` namedtuple.
+    """
+    # Seed the RNG
+    rng = np.random.default_rng(seed)
+
+    # Generate indices that'll select stats
+    pos_sel = rng.integers(2, size=(n_samples, len(hypotheses)), dtype=bool)
+
+    # Flip mask to obtain selectors for system hypotheses
+    neg_sel = ~pos_sel
+
+    if n_ar_confidence > 0:
+        # Perform confidence estimation as well
+        bs_idxs = rng.choice(
+            len(hypotheses), size=(n_ar_confidence, len(hypotheses)), replace=True)
+
+    results = {}
+
+    for name, metric in metrics.items():
+        # Use pre-computed match stats for the baseline
+        bl_stats, bl_result = baseline_info[name]
+
+        # Compute system's stats and score
+        sacrelogger.info(f'Computing {name} for {sys_name!r} and extracting sufficient statistics')
+        sys_stats = metric._extract_corpus_statistics(hypotheses, references)
+        sys_score = metric._aggregate_and_compute(sys_stats)
+
+        # original test statistic: absolute difference between baseline and the system
+        diff = abs(bl_result.score - sys_score.score)
+
+        sacrelogger.info(f' > Performing approximate randomization test (# trials: {n_samples})')
+        # get shuffled pseudo systems
+        shuf_a = pos_sel @ bl_stats + neg_sel @ sys_stats
+        shuf_b = neg_sel @ bl_stats + pos_sel @ sys_stats
+
+        # Aggregate trial stats and compute scores for each
+        scores_a = np.array(
+            [metric._aggregate_and_compute(x).score for x in shuf_a[:, None]])
+        scores_b = np.array(
+            [metric._aggregate_and_compute(x).score for x in shuf_b[:, None]])
+
+        # Count the statistical difference and compute the p-value
+        p = _compute_p_value(
+            np.abs(np.array(scores_a) - np.array(scores_b)), diff)
+
+        res = Result(sys_score.score, p)
+
+        if n_ar_confidence > 0:
+            sacrelogger.info(f' > Performing bootstrap resampling for confidence interval (# resamples: {n_ar_confidence})')
+            sys_stats = np.array(sys_stats, dtype='float32')
+            # recompute scores for all resamples
+            sys_scores = np.array([
+                metric._compute_score_from_stats(_s.sum(0)).score for _s in sys_stats[bs_idxs]
+            ])
+            res.mean, res.ci = estimate_ci(sys_scores)
+
+        # Store the result
+        results[name] = res
+
+    return sys_name, results
+
+
+def _paired_bs_test(baseline_info: Dict[str, Tuple[np.ndarray, Result]],
+                    sys_name: str,
+                    hypotheses: Sequence[str],
+                    references: Optional[Sequence[Sequence[str]]],
+                    metrics: Dict[str, Metric],
+                    n_samples: int = 1000,
+                    n_ar_confidence: int = -1,
+                    seed: Optional[int] = None) -> Tuple[str, Dict[str, Result]]:
+    """Paired bootstrap resampling test for MT evaluation. This function
+    replicates the behavior of the Moses script called
+    `bootstrap-hypothesis-difference-significance.pl`.
+
+    :param baseline_info: A dictionary with `Metric` instances as the keys,
+    that contains sufficient statistics and a `Result` instance for the baseline system.
+    :param sys_name: The name of the system to be evaluated.
+    :param hypotheses: A sequence of string hypotheses for the system.
+    :param references: A sequence of reference documents with document being
+    defined as a sequence of reference strings. If `None`, references
+    will be used through each metric's internal cache.
+    :param metrics: A dictionary of `Metric` instances that will be computed
+    for each system.
+    :param n_samples: The number of bootstrap resamples.
+    :param n_ar_confidence: This parameter is not used for this function but
+    is there for signature compatibility in the API.
+    :param seed: The seed value for the RNG. If `None`, the RNG will not be
+    fixed to a particular seed.
+
+    :return: A tuple with first element being the system name and the second
+    being a `Result` namedtuple.
+    """
+    # Seed the RNG
+    rng = np.random.default_rng(seed)
+
+    results = {}
+
+    # It takes ~10ms to generated the indices
+    idxs = rng.choice(
+        len(hypotheses), size=(n_samples, len(hypotheses)), replace=True)
+
+    for name, metric in metrics.items():
+        # Use pre-computed match stats for the baseline
+        bl_stats, bl_result = baseline_info[name]
+
+        # Compute system's stats and score
+        sacrelogger.info(f'Computing {name} for {sys_name!r} and extracting sufficient statistics')
+        sys_stats = metric._extract_corpus_statistics(hypotheses, references)
+        sys_score = metric._aggregate_and_compute(sys_stats)
+
+        # Convert to numpy arrays for efficient indexing
+        sys_stats = np.array(sys_stats, dtype='float32')
+        bl_stats = np.array(bl_stats, dtype='float32')
+
+        # original test statistic: absolute difference between baseline and the system
+        diff = abs(bl_result.score - sys_score.score)
+
+        sacrelogger.info(f' > Performing paired bootstrap resampling test (# resamples: {n_samples})')
+        scores_bl = np.array(
+            [metric._compute_score_from_stats(_s.sum(0)).score for _s in bl_stats[idxs]])
+        scores_sys = np.array(
+            [metric._compute_score_from_stats(_s.sum(0)).score for _s in sys_stats[idxs]])
+
+        # Compute CI as well
+        sys_mean, sys_ci = estimate_ci(scores_sys)
+
+        # Compute the statistics
+        sample_diffs = np.abs(scores_sys - scores_bl)
+        stats = sample_diffs - sample_diffs.mean()
+
+        # Count the statistical difference and compute the p-value
+        p = _compute_p_value(stats, diff)
+
+        results[name] = Result(sys_score.score, p, sys_mean, sys_ci)
+
+    return sys_name, results
+
+
+class PairedTest:
+    """This is the manager class that will call the actual standalone implementation
+    for approximate randomization or paired bootstrap resampling, based on the
+    `test_type` argument.
+
+    :param named_systems: A lisf of (system_name, system_hypotheses) tuples on
+    which the test will be applied.
+    :param metrics: A dictionary of `Metric` instances that will be computed
+    for each system.
+    :param references: A sequence of reference documents with document being
+    defined as a sequence of reference strings. If `None`, already cached references
+    will be used through each metric's internal cache.
+    :param test_type: `ar` for approximate randomization, `bs` for paired bootstrap.
+    :param n_samples: The number of AR trials (for `ar`) or bootstrap resamples (for `bs`).
+    The defaults (10000 or 1000 respectively) will be used if 0 is passed.
+    :param n_ar_confidence: If `approximate randomization` is selected, the number
+    of bootstrap resamples to use for confidence estimation. A value of -1 disables
+    confidence estimation. 0 will use the default of 1000.
+    :param n_jobs: If 0, a worker process will be spawned for each system variant.
+    If > 0, the number of workers will be set accordingly. The default of 1
+    does not use multi-processing.
+    """
+    _DEFAULT_SAMPLES = {
+        'ar': 10000,
+        'bs': 1000,
+    }
+
+    def __init__(self, named_systems: List[Tuple[str, Sequence[str]]],
+                 metrics: Mapping[str, Metric],
+                 references: Optional[Sequence[Sequence[str]]],
+                 test_type: str = 'ar',
+                 n_samples: int = 0,
+                 n_ar_confidence: int = -1,
+                 n_jobs: int = 1):
+        assert test_type in ('ar', 'bs'), f"Unknown test type {test_type!r}"
+        self.test_type = test_type
+
+        # Set method
+        if self.test_type == 'ar':
+            self._fn = _paired_ar_test
+        elif self.test_type == 'bs':
+            self._fn = _paired_bs_test
+
+        # Set numpy RNG's seed
+        # If  given -> Fix to the given value
+        # If given but =='[Nn]one', don't fix the seed i.e. pull entropy from OS
+        seed = os.environ.get('SACREBLEU_SEED', '12345')
+        self._seed = None if seed.lower() == 'none' else int(seed)
+        self.n_jobs = n_jobs
+        self.references = references
+        self.named_systems = named_systems
+
+        # Set the defaults if requested
+        self.n_ar_confidence = n_ar_confidence if n_ar_confidence != 0 else \
+            self._DEFAULT_SAMPLES['bs']
+
+        self.n_samples = n_samples if n_samples > 0 else \
+            self._DEFAULT_SAMPLES[self.test_type]
+
+        # Number of systems (excluding the baseline)
+        self.n_systems = len(named_systems) - 1
+
+        # Decide on number of workers
+        if IS_WINDOWS:
+            sacrelogger.warning('Parallel tests are not supported on Windows.')
+            self.n_jobs = 1
+        elif self.n_jobs == 0:
+            # Decide automatically
+            # Divide by two to ignore hyper-threading
+            n_max_jobs = mp.cpu_count() // 2
+            if n_max_jobs == 0:
+                self.n_jobs = 1
+            else:
+                # Don't use more workers than the number of CPUs
+                self.n_jobs = min(n_max_jobs, self.n_systems)
+
+        self._signatures: Dict[str, Signature] = {}
+        self._baseline_info: Dict[str, Tuple[Any, Result]] = {}
+
+        ##################################################
+        # Pre-compute and cache baseline system statistics
+        ##################################################
+        self.metrics = {}
+
+        bl_name, bl_hyps = self.named_systems[0]
+
+        for name, metric in metrics.items():
+            sacrelogger.info(f'Pre-computing {name} statistics for {bl_name!r}')
+            bl_stats = metric._extract_corpus_statistics(bl_hyps, self.references)
+            bl_score = metric._aggregate_and_compute(bl_stats)
+
+            # Compute CI for the baseline here once
+            confidence_n = self.n_samples if self.test_type == 'bs' \
+                else self.n_ar_confidence
+
+            bl_mean, bl_ci = None, None
+            if confidence_n > 0:
+                _, bl_scores = _bootstrap_resample(bl_stats, metric, confidence_n)
+                bl_mean, bl_ci = estimate_ci(np.array([x.score for x in bl_scores]))
+
+            result = Result(bl_score.score, mean=bl_mean, ci=bl_ci)
+            # Use updated name for the metric
+            self._baseline_info[bl_score.name] = (bl_stats, result)
+            self.metrics[bl_score.name] = metric
+
+            # Update metric signature as well
+            sig = metric.get_signature()
+            sig.update('seed', str(self._seed).lower())
+
+            # Num samples for bs, num trials for AR
+            sig.update(self.test_type, self.n_samples)
+            if self.n_ar_confidence > 0:
+                # Bootstrap is used for AR CI as well
+                sig.update('bs', self.n_ar_confidence)
+            self._signatures[bl_score.name] = sig
+
+    def __call__(self) -> Tuple[Dict[str, Signature], Dict[str, List[Union[str, Result]]]]:
+        """Runs the paired test either on single or multiple worker processes."""
+        tasks = []
+        scores: Dict[str, List[Union[str, Result]]] = {}
+
+        # Add the name column
+        scores['System'] = [ns[0] for ns in self.named_systems]
+
+        # Store baseline results as the first position
+        for metric, (_, result) in self._baseline_info.items():
+            scores[metric] = [result]
+
+        # Prepare list of arguments for each comparison
+        # Skip the baseline (pos: 0)
+        for idx, (name, hyps) in enumerate(self.named_systems[1:]):
+            seed = self._seed if self._seed else None
+
+            tasks.append(
+                (self._baseline_info, name, hyps, self.references,
+                 self.metrics, self.n_samples, self.n_ar_confidence, seed))
+
+        # Run the test(s)
+        if self.n_jobs == 1:
+            results = [self._fn(*args) for args in tasks]
+        else:
+            # NOTE: The overhead of worker creation is not negligible
+            # but if you have many systems and TER enabled, this significantly
+            # speeds up the test.
+            # NOTE: This only works on Linux/Mac OS X but not Windows. Windows only
+            # supports `spawn` backend which requires things to be called
+            # from within __main__.
+            sacrelogger.info(f'Launching {self.n_jobs} parallel workers.')
+            with mp.get_context('fork').Pool(self.n_jobs) as pool:
+                jobs = [pool.apply_async(self._fn, args) for args in tasks]
+
+                # wait for completion
+                results = [j.get() for j in jobs]
+
+        # Keep the order deterministic
+        for sys_name, sys_results in results:
+            for metric, _result in sys_results.items():
+                scores[metric].append(_result)
+
+        return self._signatures, scores

evalkit_eagle/lib/python3.10/site-packages/sacrebleu/utils.py

@@ -0,0 +1,639 @@
+import itertools
+import json
+import os
+import re
+import sys
+import gzip
+import math
+import hashlib
+import logging
+import portalocker
+from collections import defaultdict
+from typing import List, Optional, Sequence, Dict
+from argparse import Namespace
+
+from tabulate import tabulate
+import colorama
+
+
+# Where to store downloaded test sets.
+# Define the environment variable $SACREBLEU, or use the default of ~/.sacrebleu.
+#
+# Querying for a HOME environment variable can result in None (e.g., on Windows)
+# in which case the os.path.join() throws a TypeError. Using expanduser() is
+# a safe way to get the user's home folder.
+USERHOME = os.path.expanduser("~")
+SACREBLEU_DIR = os.environ.get('SACREBLEU', os.path.join(USERHOME, '.sacrebleu'))
+
+sacrelogger = logging.getLogger('sacrebleu')
+
+
+class Color:
+    ENABLE_COLORS = True
+
+    @staticmethod
+    def format(msg: str, color: str) -> str:
+        """Returns a colored version of the given message string.
+
+        :param msg: The string to Color.format.
+        :param color: The color specifier i.e. 'red', 'blue', 'green', etc.
+        :return: A colored version of the string if the output is a terminal.
+        """
+        if not Color.ENABLE_COLORS:
+            return msg
+        _ansi_str = getattr(colorama.Fore, color.upper(), None)
+        if _ansi_str:
+            return f'{_ansi_str}{msg}{colorama.Style.RESET_ALL}'
+
+        return msg
+
+
+def _format_score_lines(scores: dict,
+                        width: int = 2,
+                        multiline: bool = True) -> Dict[str, List[str]]:
+    """Formats the scores prior to tabulating them."""
+    new_scores = {'System': scores.pop('System')}
+    p_val_break_char = '\n' if multiline else ' '
+    is_bootstrap = False
+
+    def _color_p_value(p: float):
+        msg = f'(p = {p:.4f})'
+        if p > 0.05:
+            return Color.format(msg, 'red')
+        return msg + '*'
+
+    for metric, vals in scores.items():
+        new_vals = []
+
+        for result in vals:
+            if not isinstance(result, str):
+                # Format result instances
+                _str = f'{result.score:.{width}f}'
+                if result.mean is not None:
+                    is_bootstrap = True
+                    _str += f' ({result.mean:.{width}f} ± {result.ci:.{width}f})'
+                if result.p_value is not None:
+                    _str += p_val_break_char + _color_p_value(result.p_value)
+            else:
+                # Already formatted in non paired-test mode
+                _str = result
+
+            new_vals.append(_str)
+
+        if is_bootstrap:
+            # Change titles
+            metric += ' (μ ± 95% CI)'
+
+        new_scores[metric] = new_vals
+
+    return new_scores
+
+
+def print_results_table(results: dict, signatures: dict, args: Namespace):
+    """Prints out a nicely formatted table for multi-system evaluation mode."""
+
+    if args.format == 'json':
+        proper_json = []
+        dict_keys = list(results.keys())
+        for i in range(len(results['System'])):
+            value = {}
+            value['system'] = results['System'][i]
+            # parse metrics
+            for j in range(1, len(dict_keys)):
+                if isinstance(results[dict_keys[j]][i], str):
+                    value[dict_keys[j]] = results[dict_keys[j]][i]
+                else:
+                    # Values inside object as dict
+                    value[dict_keys[j]] = results[dict_keys[j]][i].__dict__
+            proper_json.append(value)
+
+        print(json.dumps(proper_json, indent=4))
+        return
+
+    tablefmt = args.format
+    if tablefmt in ('text'):
+        tablefmt = 'fancy_grid'
+    elif tablefmt == 'latex':
+        # Use booktabs
+        tablefmt = 'latex_booktabs'
+
+    # If paired testing has been given, this'll format the score lines
+    results = _format_score_lines(
+        results, args.width, multiline=tablefmt == 'fancy_grid')
+
+    new_dict = {}
+
+    # Color the column names and the baseline system name and scores
+    has_baseline = False
+    baseline_name = ''
+    for name in results.keys():
+        val = results[name]
+        if val[0].startswith('Baseline:') or has_baseline:
+            if val[0].startswith('Baseline:'):
+                baseline_name = val[0]
+            has_baseline = True
+            val[0] = Color.format(val[0], 'yellow')
+        new_dict[Color.format(name, 'cyan')] = results[name]
+
+    # Finally tabulate
+    table = tabulate(
+        new_dict, headers='keys', tablefmt=tablefmt,
+        colalign=('right', ),
+        stralign='center',
+        numalign='center',
+        floatfmt=f'.{args.width}f')
+
+    print(table)
+    print()
+
+    is_paired = args.paired_bs or args.paired_ar
+
+    if is_paired:
+        test_type = 'bootstrap resampling' if args.paired_bs else 'approximate randomization'
+        n_samples_or_trials = args.paired_bs_n if args.paired_bs else args.paired_ar_n
+        test_sample_type = 'resampling trials' if args.paired_bs else 'trials'
+        msg = f'Paired {test_type} test with {n_samples_or_trials} {test_sample_type}'
+
+        bline = Color.format('baseline', 'yellow')
+        bline_name = Color.format(baseline_name, 'yellow')
+        null_hyp = Color.format('Null hypothesis', 'green')
+        pval_color = Color.format('highlighted in red', 'red')
+
+        # Print fancy header
+        print('-' * len(msg) + '\n' + msg + '\n' + '-' * len(msg))
+        print(f' - Each system is pairwise compared to {bline_name}.')
+        if args.paired_bs:
+            print('   Actual system score / bootstrap estimated true mean / 95% CI are provided for each metric.')
+        else:
+            print('   Actual system score is provided for each metric.')
+        print()
+        print(f' - {null_hyp}: the system and the {bline} translations are essentially')
+        print(f'   generated by the same underlying process. For a given system and the {bline},')
+        print('   the p-value is roughly the probability of the absolute score difference (delta)')
+        print(f'   or higher occurring due to chance, under the assumption that the {null_hyp.lower()} is correct.')
+        print()
+        print(f' - Assuming a significance threshold of 0.05, the {null_hyp.lower()} can be rejected')
+        print('   for p-values < 0.05 (marked with "*"). This means that the delta is unlikely to be attributed')
+        print(f'   to chance, hence the system is significantly "different" than the {bline}.')
+        print(f'   Otherwise, the p-values are {pval_color}.')
+        print()
+        print(f' - NOTE: Significance does not tell whether a system is "better" than the {bline} but rather')
+        print('   emphasizes the "difference" of the systems in terms of the replicability of the delta.')
+        print()
+
+    print('-----------------')
+    print('Metric signatures')
+    print('-----------------')
+    for name, sig in signatures.items():
+        print(f' - {name:<10} {sig}')
+
+
+def print_single_results(results: List[str], args: Namespace):
+    """Re-process metric strings to align them nicely."""
+    if args.format == 'json':
+        if len(results) > 1:
+            proper_json = '[\n' + ',\n'.join(results) + '\n]'
+            print(proper_json)
+        else:
+            print(results[0])
+        return
+
+    # Color confidence strings for emphasis
+    if 'μ' in results[0]:
+        color_re = re.compile(r'(\(μ = [0-9\.]+ ± [0-9\.]+\))')
+        for idx in range(len(results)):
+            results[idx] = color_re.sub(
+                lambda m: Color.format(m.group(), 'cyan'), results[idx])
+
+    if len(results) == 1:
+        # Just one system, nothing to align.
+        print(results[0])
+        return
+
+    # Align by '=' character
+    lens = []
+    for line in results:
+        # If not score_only, split lines from '=' for re-alignment
+        try:
+            lens.append(line.index('=') - 1)
+        except ValueError:
+            print(line)
+
+    if len(lens) > 0:
+        w = max(lens)
+        for (_len, line) in zip(lens, results):
+            left, right = line[:_len], line[_len:]
+            print(f'{left:>{w}}{right}')
+
+
+def sanity_check_lengths(system: Sequence[str],
+                         refs: Sequence[Sequence[str]],
+                         test_set: Optional[str] = None):
+    n_hyps = len(system)
+    if any(len(ref_stream) != n_hyps for ref_stream in refs):
+        sacrelogger.error("System and reference streams have different lengths.")
+        if test_set:
+            sacrelogger.error("This could be an issue with your system output "
+                              "or with sacreBLEU's reference database if -t is given.")
+            sacrelogger.error("For the latter, try cleaning out the cache by typing:\n")
+            sacrelogger.error(f"  rm -r {SACREBLEU_DIR}/{test_set}\n")
+            sacrelogger.error("The test sets will be re-downloaded the next time you run sacreBLEU.")
+        sys.exit(1)
+
+
+def smart_open(file, mode='rt', encoding='utf-8'):
+    """Convenience function for reading compressed or plain text files.
+    :param file: The file to read.
+    :param mode: The file mode (read, write).
+    :param encoding: The file encoding.
+    """
+    if file.endswith('.gz'):
+        return gzip.open(file, mode=mode, encoding=encoding, newline="\n")
+    return open(file, mode=mode, encoding=encoding, newline="\n")
+
+
+def my_log(num: float) -> float:
+    """
+    Floors the log function
+
+    :param num: the number
+    :return: log(num) floored to a very low number
+    """
+
+    if num == 0.0:
+        return -9999999999
+    return math.log(num)
+
+
+def sum_of_lists(lists):
+    """Aggregates list of numeric lists by summing."""
+    if len(lists) == 1:
+        return lists[0]
+
+    # Preserve datatype
+    size = len(lists[0])
+    init_val = type(lists[0][0])(0.0)
+    total = [init_val] * size
+    for ll in lists:
+        for i in range(size):
+            total[i] += ll[i]
+    return total
+
+
+def args_to_dict(args, prefix: str, strip_prefix: bool = False):
+    """Filters argparse's `Namespace` into dictionary with arguments
+    beginning with the given prefix."""
+    prefix += '_'
+    d = {}
+    for k, v in args.__dict__.items():
+        if k.startswith(prefix):
+            k = k.replace(prefix, '') if strip_prefix else k
+            d[k] = v
+    return d
+
+
+def print_test_set(test_set, langpair, requested_fields, origlang=None, subset=None):
+    """Prints to STDOUT the specified side of the specified test set.
+
+    :param test_set: the test set to print
+    :param langpair: the language pair
+    :param requested_fields: the fields to print
+    :param origlang: print only sentences with a given original language (2-char ISO639-1 code), "non-" prefix means negation
+    :param subset: print only sentences whose document annotation matches a given regex
+    """
+    if test_set not in DATASETS:
+        raise Exception(f"No such test set {test_set}")
+
+    fieldnames = DATASETS[test_set].fieldnames(langpair)
+    all_files = DATASETS[test_set].get_files(langpair)
+
+    if "all" in requested_fields and len(requested_fields) != 1:
+        sacrelogger.error("Cannot use --echo all with other fields")
+        sys.exit(1)
+    elif "all" in requested_fields:
+        requested_fields = fieldnames
+
+    # backwards compatibility: allow "ref" even if not present (choose first)
+    if "ref" in requested_fields and "ref" not in fieldnames:
+        replacement_ref = min([f for f in fieldnames if f.startswith("ref")])
+        requested_fields = [f if f != "ref" else replacement_ref for f in requested_fields]
+
+    files = []
+    for field in requested_fields:
+        if field not in fieldnames:
+            sacrelogger.error(f"No such field {field} in test set {test_set} for language pair {langpair}.")
+            sacrelogger.error(f"available fields for {test_set}/{langpair}: {', '.join(fieldnames)}")
+            if "ref" not in fieldnames:
+                subref = min([f for f in fieldnames if f.startswith("ref")])
+                sacrelogger.error(f"'ref' also allowed for backwards compatibility (will return {subref})")
+            sys.exit(1)
+        index = fieldnames.index(field)
+        files.append(all_files[index])
+
+    streams = [smart_open(file) for file in files]
+    streams = filter_subset(streams, test_set, langpair, origlang, subset)
+    for lines in zip(*streams):
+        print('\t'.join(map(lambda x: x.rstrip(), lines)))
+
+
+def get_source_file(test_set: str, langpair: str) -> str:
+    """
+    Returns the source file for a given testset/langpair.
+    Downloads it first if it is not already local.
+
+    :param test_set: The test set (e.g., "wmt19")
+    :param langpair: The language pair (e.g., "de-en")
+    :return: the path to the requested source file
+    """
+    if test_set not in DATASETS:
+        raise Exception(f"No such test set {test_set}")
+
+    return DATASETS[test_set].get_source_file(langpair)
+
+
+def get_reference_files(test_set: str, langpair: str) -> List[str]:
+    """
+    Returns a list of one or more reference file paths for the given testset/langpair.
+    Downloads the references first if they are not already local.
+
+    :param test_set: The test set (e.g., "wmt19")
+    :param langpair: The language pair (e.g., "de-en")
+    :return: a list of one or more reference file paths
+    """
+    if test_set not in DATASETS:
+        raise Exception(f"No such test set {test_set}")
+    return DATASETS[test_set].get_reference_files(langpair)
+
+
+def get_files(test_set, langpair) -> List[str]:
+    """
+    Returns the path of the source file and all reference files for
+    the provided test set / language pair.
+    Downloads the references first if they are not already local.
+
+    :param test_set: The test set (e.g., "wmt19")
+    :param langpair: The language pair (e.g., "de-en")
+    :return: a list of the source file and all reference files
+    """
+
+    if test_set not in DATASETS:
+        raise Exception(f"No such test set {test_set}")
+    return DATASETS[test_set].get_files(langpair)
+
+
+def extract_tarball(filepath, destdir):
+    sacrelogger.info(f'Extracting {filepath} to {destdir}')
+    if filepath.endswith('.tar.gz') or filepath.endswith('.tgz'):
+        import tarfile
+        with tarfile.open(filepath) as tar:
+            tar.extractall(path=destdir)
+    elif filepath.endswith('.zip'):
+        import zipfile
+        with zipfile.ZipFile(filepath, 'r') as zipfile:
+            zipfile.extractall(path=destdir)
+
+
+def get_md5sum(dest_path):
+    # Check md5sum
+    md5 = hashlib.md5()
+    with open(dest_path, 'rb') as infile:
+        for line in infile:
+            md5.update(line)
+    return md5.hexdigest()
+
+
+def download_file(source_path, dest_path, extract_to=None, expected_md5=None):
+    """Downloading utility.
+
+    Downloads the specified test to the system location specified by the SACREBLEU environment variable.
+
+    :param source_path: the remote uri to download
+    :param dest_path: where to save the file
+    :param extract_to: for tarballs, where to extract to
+    :param expected_md5: the MD5 sum
+    :return: the set of processed file names
+    """
+    import urllib.request
+    import ssl
+
+    outdir = os.path.dirname(dest_path)
+    os.makedirs(outdir, exist_ok=True)
+
+    # Make sure to open in mode "a"
+    lockfile = f"{dest_path}.lock"
+    with portalocker.Lock(lockfile, timeout=60):
+
+        if not os.path.exists(dest_path) or os.path.getsize(dest_path) == 0:
+            sacrelogger.info(f"Downloading {source_path} to {dest_path}")
+
+            try:
+                with urllib.request.urlopen(source_path) as f, open(dest_path, 'wb') as out:
+                    out.write(f.read())
+            except ssl.SSLError:
+                sacrelogger.error('An SSL error was encountered in downloading the files. If you\'re on a Mac, '
+                                    'you may need to run the "Install Certificates.command" file located in the '
+                                    '"Python 3" folder, often found under /Applications')
+                sys.exit(1)
+
+            if expected_md5 is not None:
+                cur_md5 = get_md5sum(dest_path)
+                if cur_md5 != expected_md5:
+                    sacrelogger.error(f'Fatal: MD5 sum of downloaded file was incorrect (got {cur_md5}, expected {expected_md5}).')
+                    sacrelogger.error(f'Please manually delete {dest_path!r} and rerun the command.')
+                    sacrelogger.error('If the problem persists, the tarball may have changed, in which case, please contact the SacreBLEU maintainer.')
+                    sys.exit(1)
+
+            # Extract the tarball
+            if extract_to is not None:
+                extract_tarball(dest_path, extract_to)
+
+
+def download_test_set(test_set, langpair=None):
+    """Downloads the specified test to the system location specified by the SACREBLEU environment variable.
+
+    :param test_set: the test set to download
+    :param langpair: the language pair (needed for some datasets)
+    :return: the set of processed file names
+    """
+    if test_set not in DATASETS:
+        raise Exception(f"No such test set {test_set}")
+    dataset = DATASETS[test_set]
+    file_paths = dataset.get_files(langpair)
+    return file_paths
+
+
+def get_langpairs_for_testset(testset: str) -> List[str]:
+    """Return a list of language pairs for a given test set."""
+    if testset not in DATASETS:
+        return []
+    return list(DATASETS[testset].langpairs.keys())
+
+
+def get_available_testsets() -> List[str]:
+    """Return a list of available test sets."""
+    return sorted(DATASETS.keys(), reverse=True)
+
+def get_available_testsets_for_langpair(langpair: str) -> List[str]:
+    """Return a list of available test sets for a given language pair"""
+    parts = langpair.split('-')
+    srclang = parts[0]
+    trglang = parts[1]
+
+    testsets = []
+    for dataset in DATASETS.values():
+        if f'{srclang}-{trglang}' in dataset.langpairs \
+                or f'{trglang}-{srclang}' in dataset.langpairs:
+            testsets.append(dataset.name)
+
+    return testsets
+
+
+def get_available_origlangs(test_sets, langpair) -> List[str]:
+    """Return a list of origlang values according to the raw XML/SGM files."""
+    if test_sets is None:
+        return []
+
+    origlangs = set()
+    for test_set in test_sets.split(','):
+        dataset = DATASETS[test_set]
+        rawfile = os.path.join(SACREBLEU_DIR, test_set, 'raw', dataset.langpairs[langpair][0])
+        from .dataset.wmt_xml import WMTXMLDataset
+        if isinstance(dataset, WMTXMLDataset):
+            for origlang in dataset._unwrap_wmt21_or_later(rawfile)['origlang']:
+                origlangs.add(origlang)
+        if rawfile.endswith('.sgm'):
+            with smart_open(rawfile) as fin:
+                for line in fin:
+                    if line.startswith('<doc '):
+                        doc_origlang = re.sub(r'.* origlang="([^"]+)".*\n', '\\1', line)
+                        origlangs.add(doc_origlang)
+    return sorted(list(origlangs))
+
+
+def get_available_subsets(test_sets, langpair) -> List[str]:
+    """Return a list of domain values according to the raw XML files and domain/country values from the SGM files."""
+    if test_sets is None:
+        return []
+
+    subsets = set()
+    for test_set in test_sets.split(','):
+        dataset = DATASETS[test_set]
+        from .dataset.wmt_xml import WMTXMLDataset
+        if isinstance(dataset, WMTXMLDataset):
+            rawfile = os.path.join(SACREBLEU_DIR, test_set, 'raw', dataset.langpairs[langpair][0])
+            fields = dataset._unwrap_wmt21_or_later(rawfile)
+            if 'domain' in fields:
+                subsets |= set(fields['domain'])
+        elif test_set in SUBSETS:
+            subsets |= set("country:" + v.split("-")[0] for v in SUBSETS[test_set].values())
+            subsets |= set(v.split("-")[1] for v in SUBSETS[test_set].values())
+    return sorted(list(subsets))
+
+def filter_subset(systems, test_sets, langpair, origlang, subset=None):
+    """Filter sentences with a given origlang (or subset) according to the raw SGM files."""
+    if origlang is None and subset is None:
+        return systems
+    if test_sets is None or langpair is None:
+        raise ValueError('Filtering for --origlang or --subset needs a test (-t) and a language pair (-l).')
+
+    if subset is not None and subset.startswith('country:'):
+        subset = subset[8:]
+
+    re_origlang = re.compile(r'.* origlang="([^"]+)".*\n')
+    re_id = re.compile(r'.* docid="([^"]+)".*\n')
+
+    indices_to_keep = []
+    for test_set in test_sets.split(','):
+        dataset = DATASETS[test_set]
+        rawfile = os.path.join(SACREBLEU_DIR, test_set, 'raw', dataset.langpairs[langpair][0])
+        from .dataset.wmt_xml import WMTXMLDataset
+        if isinstance(dataset, WMTXMLDataset):
+            fields = dataset._unwrap_wmt21_or_later(rawfile)
+            domains = fields['domain'] if 'domain' in fields else itertools.repeat(None)
+            for doc_origlang, doc_domain in zip(fields['origlang'], domains):
+                if origlang is None:
+                    include_doc = True
+                else:
+                    if origlang.startswith('non-'):
+                        include_doc = doc_origlang != origlang[4:]
+                    else:
+                        include_doc = doc_origlang == origlang
+                if subset is not None and (doc_domain is None or not re.search(subset, doc_domain)):
+                    include_doc = False
+                indices_to_keep.append(include_doc)
+        elif rawfile.endswith('.sgm'):
+            doc_to_tags = {}
+            if subset is not None:
+                if test_set not in SUBSETS:
+                    raise Exception('No subset annotation available for test set ' + test_set)
+                doc_to_tags = SUBSETS[test_set]
+            with smart_open(rawfile) as fin:
+                include_doc = False
+                for line in fin:
+                    if line.startswith('<doc '):
+                        if origlang is None:
+                            include_doc = True
+                        else:
+                            doc_origlang = re_origlang.sub(r'\1', line)
+                            if origlang.startswith('non-'):
+                                include_doc = doc_origlang != origlang[4:]
+                            else:
+                                include_doc = doc_origlang == origlang
+
+                        if subset is not None:
+                            doc_id = re_id.sub(r'\1', line)
+                            if not re.search(subset, doc_to_tags.get(doc_id, '')):
+                                include_doc = False
+                    if line.startswith('<seg '):
+                        indices_to_keep.append(include_doc)
+        else:
+            raise Exception(f'--origlang and --subset supports only WMT *.xml and *.sgm files, not {rawfile!r}')
+    return [[sentence for sentence, keep in zip(sys, indices_to_keep) if keep] for sys in systems]
+
+
+def print_subset_results(metrics, full_system, full_refs, args):
+    w = args.width
+    origlangs = args.origlang if args.origlang else \
+        get_available_origlangs(args.test_set, args.langpair)
+
+    if len(origlangs) == 0:
+        print('No subset information found. Consider using --origlang argument.')
+        return
+
+    results = defaultdict(list)
+
+    for origlang in origlangs:
+        subsets = [None]
+        if args.subset is not None:
+            subsets += [args.subset]
+        else:
+            subsets += get_available_subsets(args.test_set, args.langpair)
+
+        for subset in subsets:
+            system, *refs = filter_subset(
+                [full_system, *full_refs], args.test_set, args.langpair, origlang, subset)
+
+            if len(system) == 0:
+                continue
+
+            key = f'origlang={origlang}'
+            if subset is None:
+                key += ' domain=ALL'
+            elif subset.startswith('country:'):
+                key += f' country={subset[8:]}'
+            else:
+                key += f' domain={subset}'
+
+            for metric in metrics.values():
+                score = metric.corpus_score(system, refs)
+                results[key].append((len(system), score))
+
+    max_left_width = max([len(k) for k in results.keys()]) + 1
+    max_metric_width = max([len(val[1].name) for val in list(results.values())[0]])
+    for key, scores in results.items():
+        key = Color.format(f'{key:<{max_left_width}}', 'yellow')
+        for n_system, score in scores:
+            print(f'{key}: sentences={n_system:<6} {score.name:<{max_metric_width}} = {score.score:.{w}f}')
+
+# import at the end to avoid circular import
+from .dataset import DATASETS, SUBSETS  # noqa: E402

evalkit_eagle/lib/python3.10/site-packages/sacrebleu/version.py

@@ -0,0 +1,16 @@
+# file generated by setuptools_scm
+# don't change, don't track in version control
+TYPE_CHECKING = False
+if TYPE_CHECKING:
+    from typing import Tuple, Union
+    VERSION_TUPLE = Tuple[Union[int, str], ...]
+else:
+    VERSION_TUPLE = object
+
+version: str
+__version__: str
+__version_tuple__: VERSION_TUPLE
+version_tuple: VERSION_TUPLE
+
+__version__ = version = '2.5.1'
+__version_tuple__ = version_tuple = (2, 5, 1)