





'''simple docstring'''

from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import (
          MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
          MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
          MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
          MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
      )


lowercase_         =			logging.get_logger(__name__)


lowercase_         =			Dict[str, Any]
lowercase_         =			List[Prediction]


@add_end_docstrings(snake_case_	)
class  a_					(    snake_case_	):

    '''simple docstring'''


    def __init__(		self			,		*A			,		**A	)		->       Tuple:
        super().__init__(*A			,		**A	)

        if self.framework == "tf":
            raise ValueError(f'The {self.__class__} is only available in PyTorch.'	)

        requires_backends(self			,		"""vision"""	)
        self.check_model_type(
            dict(
                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()	)	)


    def       snake_case_(		self			,		**A	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      {}
        _SCREAMING_SNAKE_CASE     =      {}
        if "subtask" in kwargs:
            _SCREAMING_SNAKE_CASE     =      kwargs["""subtask"""]
            _SCREAMING_SNAKE_CASE     =      kwargs["""subtask"""]
        if "threshold" in kwargs:
            _SCREAMING_SNAKE_CASE     =      kwargs["""threshold"""]
        if "mask_threshold" in kwargs:
            _SCREAMING_SNAKE_CASE     =      kwargs["""mask_threshold"""]
        if "overlap_mask_area_threshold" in kwargs:
            _SCREAMING_SNAKE_CASE     =      kwargs["""overlap_mask_area_threshold"""]

        return preprocess_kwargs, {}, postprocess_kwargs


    def __call__(		self			,		A			,		**A	)		->       Union[Predictions, List[Prediction]]:
        return super().__call__(A			,		**A	)


    def       snake_case_(		self			,		A			,		A=None	)		->       int:
        _SCREAMING_SNAKE_CASE     =      load_image(A	)
        _SCREAMING_SNAKE_CASE     =      [(image.height, image.width)]
        if self.model.config.__class__.__name__ == "OneFormerConfig":
            if subtask is None:
                _SCREAMING_SNAKE_CASE     =      {}
            else:
                _SCREAMING_SNAKE_CASE     =      {"""task_inputs""": [subtask]}
            _SCREAMING_SNAKE_CASE     =      self.image_processor(images=[image]			,		return_tensors="""pt"""			,		**A	)
            _SCREAMING_SNAKE_CASE     =      self.tokenizer(
                inputs["""task_inputs"""]			,		padding="""max_length"""			,		max_length=self.model.config.task_seq_len			,		return_tensors=self.framework			,		)["""input_ids"""]
        else:
            _SCREAMING_SNAKE_CASE     =      self.image_processor(images=[image]			,		return_tensors="""pt"""	)
        _SCREAMING_SNAKE_CASE     =      target_size
        return inputs


    def       snake_case_(		self			,		A	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      model_inputs.pop("""target_size"""	)
        _SCREAMING_SNAKE_CASE     =      self.model(**A	)
        _SCREAMING_SNAKE_CASE     =      target_size
        return model_outputs





    def       snake_case_(		self			,		A			,		A=None			,		A=0.9			,		A=0.5			,		A=0.5	)		->       str:
        _SCREAMING_SNAKE_CASE     =      None
        if subtask in {"panoptic", None} and hasattr(self.image_processor			,		"""post_process_panoptic_segmentation"""	):
            _SCREAMING_SNAKE_CASE     =      self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor			,		"""post_process_instance_segmentation"""	):
            _SCREAMING_SNAKE_CASE     =      self.image_processor.post_process_instance_segmentation

        if fn is not None:
            _SCREAMING_SNAKE_CASE     =      fn(
                A			,		threshold=A			,		mask_threshold=A			,		overlap_mask_area_threshold=A			,		target_sizes=model_outputs["""target_size"""]			,		)[0]

            _SCREAMING_SNAKE_CASE     =      []
            _SCREAMING_SNAKE_CASE     =      outputs["""segmentation"""]

            for segment in outputs["segments_info"]:
                _SCREAMING_SNAKE_CASE     =      (segmentation == segment["""id"""]) * 255
                _SCREAMING_SNAKE_CASE     =      Image.fromarray(mask.numpy().astype(np.uinta	)			,		mode="""L"""	)
                _SCREAMING_SNAKE_CASE     =      self.model.config.idalabel[segment["""label_id"""]]
                _SCREAMING_SNAKE_CASE     =      segment["""score"""]
                annotation.append({"""score""": score, """label""": label, """mask""": mask}	)

        elif subtask in {"semantic", None} and hasattr(self.image_processor			,		"""post_process_semantic_segmentation"""	):
            _SCREAMING_SNAKE_CASE     =      self.image_processor.post_process_semantic_segmentation(
                A			,		target_sizes=model_outputs["""target_size"""]	)[0]

            _SCREAMING_SNAKE_CASE     =      []
            _SCREAMING_SNAKE_CASE     =      outputs.numpy()
            _SCREAMING_SNAKE_CASE     =      np.unique(A	)

            for label in labels:
                _SCREAMING_SNAKE_CASE     =      (segmentation == label) * 255
                _SCREAMING_SNAKE_CASE     =      Image.fromarray(mask.astype(np.uinta	)			,		mode="""L"""	)
                _SCREAMING_SNAKE_CASE     =      self.model.config.idalabel[label]
                annotation.append({"""score""": None, """label""": label, """mask""": mask}	)
        else:
            raise ValueError(f'Subtask {subtask} is not supported for model {type(self.model	)}'	)
        return annotation










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase_         =			"""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowercase_         =			"""\
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 at https://github.com/mjpost/sacreBLEU for more information.
"""

lowercase_         =			"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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

    '''simple docstring'''


    def       snake_case_(		self	)		->       Union[str, Any]:
        if version.parse(scb.__version__	) < version.parse("""1.4.12"""	):
            raise ImportWarning(
                """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
                """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""	)
        return datasets.MetricInfo(
            description=_DESCRIPTION			,		citation=_CITATION			,		homepage="""https://github.com/mjpost/sacreBLEU"""			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""			,		id="""sequence"""	),
                    """references""": datasets.Sequence(datasets.Value("""string"""			,		id="""sequence"""	)			,		id="""references"""	),
                }	)			,		codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]			,		reference_urls=[
                """https://github.com/mjpost/sacreBLEU""",
                """https://en.wikipedia.org/wiki/BLEU""",
                """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
            ]			,		)





    def       snake_case_(		self			,		A			,		A			,		A="exp"			,		A=None			,		A=False			,		A=False			,		A=None			,		A=False			,		)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      len(references[0]	)
        if any(len(A	) != references_per_prediction for refs in references	):
            raise ValueError("""Sacrebleu requires the same number of references for each prediction"""	)
        _SCREAMING_SNAKE_CASE     =      [[refs[i] for refs in references] for i in range(A	)]
        _SCREAMING_SNAKE_CASE     =      scb.corpus_bleu(
            A			,		A			,		smooth_method=A			,		smooth_value=A			,		force=A			,		lowercase=A			,		use_effective_order=A			,		**({"""tokenize""": tokenize} if tokenize else {})			,		)
        _SCREAMING_SNAKE_CASE     =      {
            """score""": output.score,
            """counts""": output.counts,
            """totals""": output.totals,
            """precisions""": output.precisions,
            """bp""": output.bp,
            """sys_len""": output.sys_len,
            """ref_len""": output.ref_len,
        }
        return output_dict



