
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


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

'''

lowerCamelCase_						     =					'''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''

lowerCamelCase_						     =					'''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    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\'].
    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
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: "c" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric(\'mauve\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
'''




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

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_="auto"	,      SCREAMING_SNAKE_CASE_=-1	,      SCREAMING_SNAKE_CASE_=0.9	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=5_00	,      SCREAMING_SNAKE_CASE_="gpt2-large"	,      SCREAMING_SNAKE_CASE_=-1	,      SCREAMING_SNAKE_CASE_=10_24	,      SCREAMING_SNAKE_CASE_=25	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=25	,      ):
       UpperCamelCase__				=    compute_mauve(
           p_text=SCREAMING_SNAKE_CASE_	,      q_text=SCREAMING_SNAKE_CASE_	,      p_features=SCREAMING_SNAKE_CASE_	,      q_features=SCREAMING_SNAKE_CASE_	,      p_tokens=SCREAMING_SNAKE_CASE_	,      q_tokens=SCREAMING_SNAKE_CASE_	,      num_buckets=SCREAMING_SNAKE_CASE_	,      pca_max_data=SCREAMING_SNAKE_CASE_	,      kmeans_explained_var=SCREAMING_SNAKE_CASE_	,      kmeans_num_redo=SCREAMING_SNAKE_CASE_	,      kmeans_max_iter=SCREAMING_SNAKE_CASE_	,      featurize_model_name=SCREAMING_SNAKE_CASE_	,      device_id=SCREAMING_SNAKE_CASE_	,      max_text_length=SCREAMING_SNAKE_CASE_	,      divergence_curve_discretization_size=SCREAMING_SNAKE_CASE_	,      mauve_scaling_factor=SCREAMING_SNAKE_CASE_	,      verbose=SCREAMING_SNAKE_CASE_	,      seed=SCREAMING_SNAKE_CASE_	,      )
       return out







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )




@require_vision
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    tempfile.mkdtemp()

       UpperCamelCase__				=    BlipImageProcessor()
       UpperCamelCase__				=    GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""						)
       UpperCamelCase__				=    BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)

       UpperCamelCase__				=    InstructBlipProcessor(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       processor.save_pretrained(self.tmpdirname						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).tokenizer







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).image_processor







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).qformer_tokenizer







  def 	UpperCAmelCase_ (self						):
       shutil.rmtree(self.tmpdirname						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    [np.random.randint(2_55	,      size=(3, 30, 4_00)	,      dtype=np.uinta						)]

       UpperCamelCase__				=    [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_	,      0	,      -1						)						) for x in image_inputs]

       return image_inputs







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
       processor.save_pretrained(self.tmpdirname						)

       UpperCamelCase__				=    self.get_tokenizer(bos_token="""(BOS)"""	,      eos_token="""(EOS)"""						)
       UpperCamelCase__				=    self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_	,      padding_value=1.0						)

       UpperCamelCase__				=    InstructBlipProcessor.from_pretrained(
           self.tmpdirname	,      bos_token="""(BOS)"""	,      eos_token="""(EOS)"""	,      do_normalize=SCREAMING_SNAKE_CASE_	,      padding_value=1.0						)

       self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()						)
       self.assertIsInstance(processor.tokenizer	,      SCREAMING_SNAKE_CASE_						)

       self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()						)
       self.assertIsInstance(processor.image_processor	,      SCREAMING_SNAKE_CASE_						)
       self.assertIsInstance(processor.qformer_tokenizer	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""np"""						)
       UpperCamelCase__				=    processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""np"""						)

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_token_type_ids=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    qformer_tokenizer(SCREAMING_SNAKE_CASE_	,      return_token_type_ids=SCREAMING_SNAKE_CASE_						)

       for key in encoded_tokens.keys():
            self.assertListEqual(encoded_tokens[key]	,      encoded_processor[key]						)

       for key in encoded_tokens_qformer.keys():
            self.assertListEqual(encoded_tokens_qformer[key]	,      encoded_processor["""qformer_""" + key]						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""
       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(
           list(inputs.keys()						)	,      ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]	,      )

       # test if it raises when no input is passed
       with pytest.raises(SCREAMING_SNAKE_CASE_						):
            processor()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase__				=    processor.batch_decode(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""
       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(
           list(inputs.keys()						)	,      ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]	,      )





