





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


_snake_case    :       Dict		       =    '\\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'

_snake_case    :       Tuple		       =    '\\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'

_snake_case    :       List[str]		       =    '\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 							_UpperCAmelCase       (       datasets.Metric       ):

  """simple docstring"""


  def 							lowercase						(							self					:						Optional[Any]	)				->		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 							lowercase						(							self					:						str   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						List[str]=None   ,    lowerCAmelCase_					:						Optional[int]=None   ,    lowerCAmelCase_					:						Union[str, Any]=None   ,    lowerCAmelCase_					:						List[Any]=None   ,    lowerCAmelCase_					:						Any="auto"   ,    lowerCAmelCase_					:						List[Any]=-1   ,    lowerCAmelCase_					:						int=0.9   ,    lowerCAmelCase_					:						Any=5   ,    lowerCAmelCase_					:						Union[str, Any]=5_0_0   ,    lowerCAmelCase_					:						Union[str, Any]="gpt2-large"   ,    lowerCAmelCase_					:						Dict=-1   ,    lowerCAmelCase_					:						str=1_0_2_4   ,    lowerCAmelCase_					:						int=2_5   ,    lowerCAmelCase_					:						List[str]=5   ,    lowerCAmelCase_					:						Any=True   ,    lowerCAmelCase_					:						str=2_5   ,    )				->		Optional[Any]:
     __lowerCAmelCase				      =      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



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  '''decision_transformer'''
						_lowercase						: Optional[Any]										=  ['''past_key_values''']
						_lowercase						: str										=  {
						    '''max_position_embeddings''': '''n_positions''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}




						def __init__(   self       ,  _lowercase=17       ,  _lowercase=4       ,  _lowercase=128       ,  _lowercase=4_096       ,  _lowercase=True       ,  _lowercase=1       ,  _lowercase=1_024       ,  _lowercase=3       ,  _lowercase=1       ,  _lowercase=None       ,  _lowercase="relu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=1e-5       ,  _lowercase=0.02       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase=50_256       ,  _lowercase=50_256       ,  _lowercase=False       ,  _lowercase=False       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					state_dim
												_lowerCAmelCase    		=					act_dim
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					max_ep_len
												_lowerCAmelCase    		=					action_tanh
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					n_positions
												_lowerCAmelCase    		=					n_layer
												_lowerCAmelCase    		=					n_head
												_lowerCAmelCase    		=					n_inner
												_lowerCAmelCase    		=					activation_function
												_lowerCAmelCase    		=					resid_pdrop
												_lowerCAmelCase    		=					embd_pdrop
												_lowerCAmelCase    		=					attn_pdrop
												_lowerCAmelCase    		=					layer_norm_epsilon
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					scale_attn_weights
												_lowerCAmelCase    		=					use_cache
												_lowerCAmelCase    		=					scale_attn_by_inverse_layer_idx
												_lowerCAmelCase    		=					reorder_and_upcast_attn

												_lowerCAmelCase    		=					bos_token_id
												_lowerCAmelCase    		=					eos_token_id

												super().__init__(bos_token_id=_lowercase       ,  eos_token_id=_lowercase       ,  **_lowercase )
