


'''simple docstring'''

from typing import Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCamelCase							:   Optional[int]  						=	logging.get_logger(__name__)


lowerCamelCase							:   str  						=	Dict[str, Any]
lowerCamelCase							:   Dict  						=	List[Prediction]
@add_end_docstrings(_lowerCAmelCase     )
class   __lowerCAmelCase						(_lowerCAmelCase     ):





      '''simple docstring'''






      def __init__(self    :    Dict   ,						*UpperCamelCase    :    Any   ,						**UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







             super().__init__(*_lowerCamelCase   ,						**_lowerCamelCase )

             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_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						**UpperCamelCase    :    str ):

             '''simple docstring'''







             lowercase__      			= {}
             if "threshold" in kwargs:
                    lowercase__      			= kwargs['''threshold''']
             return {}, {}, postprocess_kwargs



      def __call__(self    :    List[str]   ,						*UpperCamelCase    :    List[str]   ,						**UpperCamelCase    :    Optional[int] ):

             '''simple docstring'''







             return super().__call__(*_lowerCamelCase   ,						**_lowerCamelCase )



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    Dict ):

             '''simple docstring'''







             lowercase__      			= load_image(_lowerCamelCase )
             lowercase__      			= torch.IntTensor([[image.height, image.width]] )
             lowercase__      			= self.image_processor(images=[image]   ,						return_tensors='''pt''' )
             if self.tokenizer is not None:
                    lowercase__      			= self.tokenizer(text=inputs['''words''']   ,						boxes=inputs['''boxes''']   ,						return_tensors='''pt''' )
             lowercase__      			= target_size
             return inputs



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    str ):

             '''simple docstring'''







             lowercase__      			= model_inputs.pop('''target_size''' )
             lowercase__      			= self.model(**_lowerCamelCase )
             lowercase__      			= outputs.__class__({'''target_size''': target_size, **outputs} )
             if self.tokenizer is not None:
                    lowercase__      			= model_inputs['''bbox''']
             return model_outputs



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[Any]=0.9 ):

             '''simple docstring'''







             lowercase__      			= model_outputs['''target_size''']
             if self.tokenizer is not None:
                    # This is a LayoutLMForTokenClassification variant.
                    # The OCR got the boxes and the model classified the words.
                    lowercase__			,lowercase__      			= target_size[0].tolist()

                    def unnormalize(UpperCamelCase    :    Tuple ):
                           return self._get_bounding_box(
                               torch.Tensor(
                                   [
                                       (width * bbox[0] / 1000),
                                       (height * bbox[1] / 1000),
                                       (width * bbox[2] / 1000),
                                       (height * bbox[3] / 1000),
                                   ] ) )

                    lowercase__			,lowercase__      			= model_outputs['''logits'''].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
                    lowercase__      			= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
                    lowercase__      			= [unnormalize(_lowerCamelCase ) for bbox in model_outputs['''bbox'''].squeeze(0 )]
                    lowercase__      			= ['''score''', '''label''', '''box''']
                    lowercase__      			= [dict(zip(_lowerCamelCase   ,						_lowerCamelCase ) ) for vals in zip(scores.tolist()   ,						_lowerCamelCase   ,						_lowerCamelCase ) if vals[0] > threshold]
             else:
                    # This is a regular ForObjectDetectionModel
                    lowercase__      			= self.image_processor.post_process_object_detection(_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase )
                    lowercase__      			= raw_annotations[0]
                    lowercase__      			= raw_annotation['''scores''']
                    lowercase__      			= raw_annotation['''labels''']
                    lowercase__      			= raw_annotation['''boxes''']

                    lowercase__      			= scores.tolist()
                    lowercase__      			= [self.model.config.idalabel[label.item()] for label in labels]
                    lowercase__      			= [self._get_bounding_box(_lowerCamelCase ) for box in boxes]

                    # {"scores": [...], ...} --> [{"score":x, ...}, ...]
                    lowercase__      			= ['''score''', '''label''', '''box''']
                    lowercase__      			= [
                        dict(zip(_lowerCamelCase   ,						_lowerCamelCase ) )
                        for vals in zip(raw_annotation['''scores''']   ,						raw_annotation['''labels''']   ,						raw_annotation['''boxes'''] )
                    ]

             return annotation






      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    "torch.Tensor" ):

             '''simple docstring'''







             if self.framework != "pt":
                    raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.''' )
             lowercase__			,lowercase__			,lowercase__			,lowercase__      			= box.int().tolist()
             lowercase__      			= {
                 '''xmin''': xmin,
                 '''ymin''': ymin,
                 '''xmax''': xmax,
                 '''ymax''': ymax,
             }
             return bbox












"""simple docstring"""


import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


lowercase_				     =     '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n'

lowercase_				     =     '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'

lowercase_				     =     '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: "c" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric(\'mauve\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n'


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


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     return datasets.MetricInfo(
         description=_DESCRIPTION,       citation=_CITATION,       homepage='''https://github.com/krishnap25/mauve''',       inputs_description=_KWARGS_DESCRIPTION,       features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string''',       id='''sequence'''  ),
                 '''references''': datasets.Value('''string''',       id='''sequence'''  ),
             }  ),       codebase_urls=['''https://github.com/krishnap25/mauve'''],       reference_urls=[
             '''https://arxiv.org/abs/2102.01454''',
             '''https://github.com/krishnap25/mauve''',
         ],       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Tuple=None,       _lowerCamelCase :     Optional[Any]=None,       _lowerCamelCase :     Union[str, Any]=None,       _lowerCamelCase :     str="auto",       _lowerCamelCase :     Union[str, Any]=-1,       _lowerCamelCase :     List[str]=0.9,       _lowerCamelCase :     int=5,       _lowerCamelCase :     Tuple=5_00,       _lowerCamelCase :     Union[str, Any]="gpt2-large",       _lowerCamelCase :     int=-1,       _lowerCamelCase :     Union[str, Any]=10_24,       _lowerCamelCase :     Union[str, Any]=25,       _lowerCamelCase :     str=5,       _lowerCamelCase :     Any=True,       _lowerCamelCase :     Union[str, Any]=25,       ):

     '''simple docstring'''
     __A									= compute_mauve(
         p_text=_lowerCamelCase,       q_text=_lowerCamelCase,       p_features=_lowerCamelCase,       q_features=_lowerCamelCase,       p_tokens=_lowerCamelCase,       q_tokens=_lowerCamelCase,       num_buckets=_lowerCamelCase,       pca_max_data=_lowerCamelCase,       kmeans_explained_var=_lowerCamelCase,       kmeans_num_redo=_lowerCamelCase,       kmeans_max_iter=_lowerCamelCase,       featurize_model_name=_lowerCamelCase,       device_id=_lowerCamelCase,       max_text_length=_lowerCamelCase,       divergence_curve_discretization_size=_lowerCamelCase,       mauve_scaling_factor=_lowerCamelCase,       verbose=_lowerCamelCase,       seed=_lowerCamelCase,       )
     return out




