





"""simple docstring"""





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCamelCase							=      '\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

_UpperCamelCase							=      '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

_UpperCamelCase							=      '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,  _KWARGS_DESCRIPTION   )
class 		SCREAMING_SNAKE_CASE_							(    datasets.Metric   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Optional[Any]			):
											return datasets.MetricInfo(
											    description=_DESCRIPTION						,    citation=_CITATION						,    inputs_description=_KWARGS_DESCRIPTION						,    features=datasets.Features(
											        {
											            '''predictions''': datasets.Sequence(datasets.Value('''string'''						,    id='''token'''			)						,    id='''sequence'''			),
											            '''references''': datasets.Sequence(
											                datasets.Sequence(datasets.Value('''string'''						,    id='''token'''			)						,    id='''sequence'''			)						,    id='''references'''			),
											        }			)						,    )




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :List[List[List[str]]]						,    __lowercase  :List[List[str]]						,    __lowercase  :int = 1						,    __lowercase  :int = 4						,    ):
											return {
											    "google_bleu": gleu_score.corpus_gleu(
											        list_of_references=__lowercase						,    hypotheses=__lowercase						,    min_len=__lowercase						,    max_len=__lowercase			)
											}











"""simple docstring"""





from typing import List, Optional, Union

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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""time_series_transformer"""
				__snake_case   :       Any	 =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}




				def __init__(     self  :List[Any]						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[int] = None						,    __lowercase  :str = "student_t"						,    __lowercase  :str = "nll"						,    __lowercase  :int = 1						,    __lowercase  :List[int] = [1, 2, 3, 4, 5, 6, 7]						,    __lowercase  :Optional[Union[str, bool]] = "mean"						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :int = 32						,    __lowercase  :int = 32						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :bool = True						,    __lowercase  :str = "gelu"						,    __lowercase  :int = 64						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :int = 100						,    __lowercase  :float = 0.02						,    __lowercase  :int=True						,    **__lowercase  :Optional[Any]						,    ):
											# time series specific configuration
											__lowerCamelCase      :      Tuple         =prediction_length
											__lowerCamelCase      :      List[Any]         =context_length or prediction_length
											__lowerCamelCase      :      Dict         =distribution_output
											__lowerCamelCase      :      str         =loss
											__lowerCamelCase      :      Tuple         =input_size
											__lowerCamelCase      :      int         =num_time_features
											__lowerCamelCase      :      int         =lags_sequence
											__lowerCamelCase      :      Optional[int]         =scaling
											__lowerCamelCase      :      str         =num_dynamic_real_features
											__lowerCamelCase      :      Optional[Any]         =num_static_real_features
											__lowerCamelCase      :      List[Any]         =num_static_categorical_features
											if cardinality and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      Optional[int]         =cardinality
											else:
																		__lowerCamelCase      :      str         =[0]
											if embedding_dimension and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      int         =embedding_dimension
											else:
																		__lowerCamelCase      :      Optional[int]         =[min(50						,    (cat + 1) // 2			) for cat in self.cardinality]
											__lowerCamelCase      :      List[str]         =num_parallel_samples

											# Transformer architecture configuration
											__lowerCamelCase      :      str         =input_size * len(__lowercase			) + self._number_of_features
											__lowerCamelCase      :      Union[str, Any]         =d_model
											__lowerCamelCase      :      int         =encoder_attention_heads
											__lowerCamelCase      :      int         =decoder_attention_heads
											__lowerCamelCase      :      Dict         =encoder_ffn_dim
											__lowerCamelCase      :      int         =decoder_ffn_dim
											__lowerCamelCase      :      List[Any]         =encoder_layers
											__lowerCamelCase      :      int         =decoder_layers

											__lowerCamelCase      :      Union[str, Any]         =dropout
											__lowerCamelCase      :      Optional[Any]         =attention_dropout
											__lowerCamelCase      :      List[str]         =activation_dropout
											__lowerCamelCase      :      List[str]         =encoder_layerdrop
											__lowerCamelCase      :      int         =decoder_layerdrop

											__lowerCamelCase      :      Tuple         =activation_function
											__lowerCamelCase      :      str         =init_std

											__lowerCamelCase      :      Dict         =use_cache

											super().__init__(is_encoder_decoder=__lowercase						,    **__lowercase			)




				@property
				def 					__lowercase  (     self  :int			):
											return (
											    sum(self.embedding_dimension			)
											    + self.num_dynamic_real_features
											    + self.num_time_features
											    + self.num_static_real_features
											    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
											)




