



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


__UpperCAmelCase	       =	'''\
@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}
}

'''

__UpperCAmelCase	       =	'''\
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
'''

__UpperCAmelCase	       =	'''
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     lowerCAmelCase_		(  datasets.Metric							):
       def  snake_case_	(      self	)					->		Union[str, Any]:
          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  snake_case_	(      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_=500,      SCREAMING_SNAKE_CASE_="gpt2-large",      SCREAMING_SNAKE_CASE_=-1,      SCREAMING_SNAKE_CASE_=1024,      SCREAMING_SNAKE_CASE_=25,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=25,      )					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     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 unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.generation import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )



@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  List[str]						     =     jnp.ones((batch_size, length)	) / length
          return scores
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Optional[Any]						     =     None
          UpperCamelCase   :  Optional[int]						     =     20

          UpperCamelCase   :  Optional[Any]						     =     self._get_uniform_logits(batch_size=2,      length=SCREAMING_SNAKE_CASE_	)

          # tweak scores to not be uniform anymore
          UpperCamelCase   :  Dict						     =     scores.at[1, 5].set((1 / length) + 0.1	)  # peak, 1st batch
          UpperCamelCase   :  Any						     =     scores.at[1, 10].set((1 / length) - 0.4	)  # valley, 1st batch

          # compute softmax
          UpperCamelCase   :  List[str]						     =     jax.nn.softmax(SCREAMING_SNAKE_CASE_,      axis=-1	)

          UpperCamelCase   :  List[Any]						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  int						     =     FlaxTemperatureLogitsWarper(temperature=1.3	)

          UpperCamelCase   :  Tuple						     =     jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE_,      scores.copy(),      cur_len=SCREAMING_SNAKE_CASE_	),      axis=-1	)
          UpperCamelCase   :  Any						     =     jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE_,      scores.copy(),      cur_len=SCREAMING_SNAKE_CASE_	),      axis=-1	)

          # uniform distribution stays uniform
          self.assertTrue(jnp.allclose(probs[0, :],      warped_prob_sharp[0, :],      atol=1e-3	)	)
          self.assertTrue(jnp.allclose(probs[0, :],      warped_prob_smooth[0, :],      atol=1e-3	)	)

          # sharp peaks get higher, valleys get lower
          self.assertLess(probs[1, :].max(),      warped_prob_sharp[1, :].max()	)
          self.assertGreater(probs[1, :].min(),      warped_prob_sharp[1, :].min()	)

          # smooth peaks get lower, valleys get higher
          self.assertGreater(probs[1, :].max(),      warped_prob_smooth[1, :].max()	)
          self.assertLess(probs[1, :].min(),      warped_prob_smooth[1, :].min()	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Dict						     =     None
          UpperCamelCase   :  Any						     =     10
          UpperCamelCase   :  Any						     =     2

          # create ramp distribution
          UpperCamelCase   :  List[Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, vocab_size)	).copy()
          UpperCamelCase   :  Tuple						     =     ramp_logits[1:, : vocab_size // 2] + vocab_size

          UpperCamelCase   :  Dict						     =     FlaxTopKLogitsWarper(3	)

          UpperCamelCase   :  Tuple						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # check that correct tokens are filtered
          self.assertListEqual(jnp.isinf(scores[0]	).tolist(),      7 * [True] + 3 * [False]	)
          self.assertListEqual(jnp.isinf(scores[1]	).tolist(),      2 * [True] + 3 * [False] + 5 * [True]	)

          # check special case
          UpperCamelCase   :  Optional[int]						     =     5
          UpperCamelCase   :  Optional[int]						     =     FlaxTopKLogitsWarper(top_k=1,      filter_value=0.0,      min_tokens_to_keep=3	)

          UpperCamelCase   :  Union[str, Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, length)	).copy()
          UpperCamelCase   :  List[str]						     =     top_k_warp_safety_check(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
          self.assertListEqual((scores == 0.0).sum(axis=-1	).tolist(),      [2, 2]	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  int						     =     None
          UpperCamelCase   :  List[str]						     =     10
          UpperCamelCase   :  Optional[Any]						     =     2

          # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
          UpperCamelCase   :  Optional[int]						     =     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]	)	)

          UpperCamelCase   :  Optional[Any]						     =     FlaxTopPLogitsWarper(0.8	)
          UpperCamelCase   :  int						     =     np.exp(top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)	)

          # dist should be filtered to keep min num values so that sum is >= top_p
          # exp (-inf) => 0
          UpperCamelCase   :  Any						     =     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]	)
          self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # check edge cases with negative and extreme logits
          UpperCamelCase   :  Optional[Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, vocab_size)	).copy() - (
              vocab_size // 2
          )

          # make ramp_logits more extreme
          UpperCamelCase   :  Tuple						     =     ramp_logits[1] * 1_00.0

          # make sure at least 2 tokens are kept
          UpperCamelCase   :  int						     =     FlaxTopPLogitsWarper(0.9,      min_tokens_to_keep=2,      filter_value=0.0	)
          UpperCamelCase   :  List[str]						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
          self.assertListEqual((filtered_dist != 0.0).sum(axis=-1	).tolist(),      [3, 2]	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Union[str, Any]						     =     20
          UpperCamelCase   :  Union[str, Any]						     =     4
          UpperCamelCase   :  Optional[int]						     =     0

          UpperCamelCase   :  Dict						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that min length is applied at length 5
          UpperCamelCase   :  List[str]						     =     ids_tensor((batch_size, 20),      vocab_size=20	)
          UpperCamelCase   :  Any						     =     5
          UpperCamelCase   :  Tuple						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     min_dist_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(),      4 * [-float('inf'	)]	)

          # check that min length is not applied anymore at length 15
          UpperCamelCase   :  Any						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     15
          UpperCamelCase   :  str						     =     min_dist_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  str						     =     20
          UpperCamelCase   :  List[Any]						     =     4
          UpperCamelCase   :  List[str]						     =     0

          UpperCamelCase   :  int						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that all scores are -inf except the bos_token_id score
          UpperCamelCase   :  Any						     =     ids_tensor((batch_size, 1),      vocab_size=20	)
          UpperCamelCase   :  List[Any]						     =     1
          UpperCamelCase   :  Tuple						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]	).all()	)
          self.assertListEqual(scores[:, bos_token_id].tolist(),      4 * [0]	)  # score for bos_token_id shold be zero

          # check that bos_token_id is not forced if current length is greater than 1
          UpperCamelCase   :  Dict						     =     3
          UpperCamelCase   :  str						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Union[str, Any]						     =     20
          UpperCamelCase   :  Optional[Any]						     =     4
          UpperCamelCase   :  List[Any]						     =     0
          UpperCamelCase   :  int						     =     5

          UpperCamelCase   :  Tuple						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that all scores are -inf except the eos_token_id when max_length is reached
          UpperCamelCase   :  str						     =     ids_tensor((batch_size, 4),      vocab_size=20	)
          UpperCamelCase   :  Tuple						     =     4
          UpperCamelCase   :  Union[str, Any]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]	).all()	)
          self.assertListEqual(scores[:, eos_token_id].tolist(),      4 * [0]	)  # score for eos_token_id should be zero

          # check that eos_token_id is not forced if max_length is not reached
          UpperCamelCase   :  str						     =     3
          UpperCamelCase   :  List[Any]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  int						     =     4
          UpperCamelCase   :  Tuple						     =     10
          UpperCamelCase   :  str						     =     15
          UpperCamelCase   :  List[str]						     =     2
          UpperCamelCase   :  Any						     =     1
          UpperCamelCase   :  List[str]						     =     15

          # dummy input_ids and scores
          UpperCamelCase   :  Dict						     =     ids_tensor((batch_size, sequence_length),      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     input_ids.copy()

          UpperCamelCase   :  Any						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     scores.copy()

          # instantiate all dist processors
          UpperCamelCase   :  List[str]						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  Optional[Any]						     =     FlaxTopKLogitsWarper(3	)
          UpperCamelCase   :  Optional[int]						     =     FlaxTopPLogitsWarper(0.8	)

          # instantiate all logits processors
          UpperCamelCase   :  Optional[Any]						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     10

          # no processor list
          UpperCamelCase   :  Any						     =     temp_dist_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     min_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     bos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     eos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # with processor list
          UpperCamelCase   :  List[str]						     =     FlaxLogitsProcessorList(
              [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]	)
          UpperCamelCase   :  Optional[Any]						     =     processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # scores should be equal
          self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist(),      input_ids_comp.tolist()	)






       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Optional[Any]						     =     4
          UpperCamelCase   :  Tuple						     =     10
          UpperCamelCase   :  Union[str, Any]						     =     15
          UpperCamelCase   :  Union[str, Any]						     =     2
          UpperCamelCase   :  Optional[Any]						     =     1
          UpperCamelCase   :  int						     =     15

          # dummy input_ids and scores
          UpperCamelCase   :  Dict						     =     ids_tensor((batch_size, sequence_length),      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     input_ids.copy()

          UpperCamelCase   :  Optional[int]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     scores.copy()

          # instantiate all dist processors
          UpperCamelCase   :  Dict						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  Optional[Any]						     =     FlaxTopKLogitsWarper(3	)
          UpperCamelCase   :  Union[str, Any]						     =     FlaxTopPLogitsWarper(0.8	)

          # instantiate all logits processors
          UpperCamelCase   :  str						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     10

          # no processor list
          def run_no_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Optional[Any]						     =     temp_dist_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  str						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Any						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     min_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     bos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     eos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             return scores

          # with processor list
          def run_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Tuple						     =     FlaxLogitsProcessorList(
                 [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]	)
             UpperCamelCase   :  Union[str, Any]						     =     processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             return scores

          UpperCamelCase   :  Dict						     =     jax.jit(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     jax.jit(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     jitted_run_no_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     jitted_run_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          # scores should be equal
          self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist(),      input_ids_comp.tolist()	)



