



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__      :				str   	=			logging.get_logger(__name__)

a__      :				int   	=			{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[Any]       				=    "marian"
   A    :  int       				=    ["past_key_values"]
   A    :  Any       				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(						self  :			List[str]			,				lowerCAmelCase  :			Tuple=5_81_01			,				lowerCAmelCase  :			Union[str, Any]=None			,				lowerCAmelCase  :			Any=10_24			,				lowerCAmelCase  :			Optional[int]=12			,				lowerCAmelCase  :			List[str]=40_96			,				lowerCAmelCase  :			Any=16			,				lowerCAmelCase  :			Union[str, Any]=12			,				lowerCAmelCase  :			Tuple=40_96			,				lowerCAmelCase  :			Union[str, Any]=16			,				lowerCAmelCase  :			Union[str, Any]=0.0			,				lowerCAmelCase  :			List[Any]=0.0			,				lowerCAmelCase  :			Dict=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Optional[int]="gelu"			,				lowerCAmelCase  :			Tuple=10_24			,				lowerCAmelCase  :			Union[str, Any]=0.1			,				lowerCAmelCase  :			Dict=0.0			,				lowerCAmelCase  :			int=0.0			,				lowerCAmelCase  :			int=0.02			,				lowerCAmelCase  :			List[str]=5_81_00			,				lowerCAmelCase  :			int=False			,				lowerCAmelCase  :			int=5_81_00			,				lowerCAmelCase  :			Optional[int]=0			,				lowerCAmelCase  :			Any=0			,				lowerCAmelCase  :			List[Any]=True			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				Any:




         """simple docstring"""

         lowercase__     =  vocab_size
         lowercase__     =  decoder_vocab_size or vocab_size
         lowercase__     =  max_position_embeddings
         lowercase__     =  d_model
         lowercase__     =  encoder_ffn_dim
         lowercase__     =  encoder_layers
         lowercase__     =  encoder_attention_heads
         lowercase__     =  decoder_ffn_dim
         lowercase__     =  decoder_layers
         lowercase__     =  decoder_attention_heads
         lowercase__     =  dropout
         lowercase__     =  attention_dropout
         lowercase__     =  activation_dropout
         lowercase__     =  activation_function
         lowercase__     =  init_std
         lowercase__     =  encoder_layerdrop
         lowercase__     =  decoder_layerdrop
         lowercase__     =  use_cache
         lowercase__     =  encoder_layers
         lowercase__     =  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__     =  share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				is_encoder_decoder=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				forced_eos_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def 			UpperCAmelCase		(						self  :			Dict)	->				Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__     =  OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])

               if self.use_past:
                     lowercase__     =  {0: 'batch'}
                     lowercase__     =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
               else:
                     lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}
                     lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}

               if self.use_past:
                     self.fill_with_past_key_values_(lowerCAmelCase			,				direction='inputs')
         elif self.task == "causal-lm":
               # TODO: figure this case out.
               lowercase__     =  OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])
               if self.use_past:
                     lowercase__,	lowercase__     =  self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__     =  {0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__     =  {0: 'batch', 2: 'past_sequence + sequence'}
         else:
               lowercase__     =  OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                       ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                       ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                   ])

         return common_inputs


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 			UpperCAmelCase		(						self  :			List[str])	->				Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__     =  super().outputs
         else:
               lowercase__     =  super(lowerCAmelCase			,				self).outputs
               if self.use_past:
                     lowercase__,	lowercase__     =  self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__     =  {0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__     =  {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			PreTrainedTokenizer			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			bool = False			,				lowerCAmelCase  :			Optional[TensorType] = None			,				)	->				Mapping[str, Any]:




         """simple docstring"""

         lowercase__     =  self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)

         # Generate decoder inputs
         lowercase__     =  seq_length if not self.use_past else 1
         lowercase__     =  self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
         lowercase__     =  {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         lowercase__     =  dict(**lowerCAmelCase			,				**lowerCAmelCase)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                     import torch
               lowercase__,	lowercase__     =  common_inputs['input_ids'].shape
               lowercase__     =  common_inputs['decoder_input_ids'].shape[1]
               lowercase__,	lowercase__     =  self.num_attention_heads
               lowercase__     =  (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               lowercase__     =  decoder_seq_length + 3
               lowercase__     =  (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               lowercase__     =  torch.cat(
                   [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase			,				lowerCAmelCase)]			,				dim=1)

               lowercase__     =  []
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               lowercase__,	lowercase__     =  self.num_layers
               lowercase__     =  min(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  max(lowerCAmelCase			,				lowerCAmelCase) - min_num_layers
               lowercase__     =  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

               for _ in range(lowerCAmelCase):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                         ))
               # TODO: test this.
               lowercase__     =  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
               for _ in range(lowerCAmelCase			,				lowerCAmelCase):
                     common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)))
         return common_inputs


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			PreTrainedTokenizer			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			bool = False			,				lowerCAmelCase  :			Optional[TensorType] = None			,				)	->				Mapping[str, Any]:




         """simple docstring"""

         lowercase__     =  self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                     import torch
               lowercase__,	lowercase__     =  common_inputs['input_ids'].shape
               # Not using the same length for past_key_values
               lowercase__     =  seqlen + 2
               lowercase__,	lowercase__     =  self.num_layers
               lowercase__,	lowercase__     =  self.num_attention_heads
               lowercase__     =  (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               lowercase__     =  common_inputs['attention_mask'].dtype
               lowercase__     =  torch.cat(
                   [common_inputs['attention_mask'], torch.ones(lowerCAmelCase			,				lowerCAmelCase			,				dtype=lowerCAmelCase)]			,				dim=1)
               lowercase__     =  [
                   (torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)) for _ in range(lowerCAmelCase)
               ]
         return common_inputs


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			PreTrainedTokenizer			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			bool = False			,				lowerCAmelCase  :			Optional[TensorType] = None			,				)	->				Mapping[str, Any]:




         """simple docstring"""

         lowercase__     =  compute_effective_axis_dimension(
             lowerCAmelCase			,				fixed_dimension=OnnxConfig.default_fixed_batch			,				num_token_to_add=0)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         lowercase__     =  tokenizer.num_special_tokens_to_add(lowerCAmelCase)
         lowercase__     =  compute_effective_axis_dimension(
             lowerCAmelCase			,				fixed_dimension=OnnxConfig.default_fixed_sequence			,				num_token_to_add=lowerCAmelCase)

         # Generate dummy inputs according to compute batch and sequence
         lowercase__     =  [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
         lowercase__     =  dict(tokenizer(lowerCAmelCase			,				return_tensors=lowerCAmelCase))
         return common_inputs


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			PreTrainedTokenizer			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			int = -1			,				lowerCAmelCase  :			bool = False			,				lowerCAmelCase  :			Optional[TensorType] = None			,				)	->				Mapping[str, Any]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__     =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   lowerCAmelCase			,				batch_size=lowerCAmelCase			,				seq_length=lowerCAmelCase			,				is_pair=lowerCAmelCase			,				framework=lowerCAmelCase)

         else:
               lowercase__     =  self._generate_dummy_inputs_for_causal_lm(
                   lowerCAmelCase			,				batch_size=lowerCAmelCase			,				seq_length=lowerCAmelCase			,				is_pair=lowerCAmelCase			,				framework=lowerCAmelCase)

         return common_inputs


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[Any])	->				Any:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__     =  super()._flatten_past_key_values_(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
         else:
               lowercase__     =  super(lowerCAmelCase			,				self)._flatten_past_key_values_(
                   lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)



   @property
   def 			UpperCAmelCase		(						self  :			Tuple)	->				float:




         """simple docstring"""

         return 1E-4





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a__      :				int   	=			"\\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"

a__      :				Optional[Any]   	=			"\\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"

a__      :				Tuple   	=			"\\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       UpperCAmelCase__(							datasets.Metric	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[Any])	->				MetricInfo:




         """simple docstring"""

         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 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			List[List[List[str]]]			,				lowerCAmelCase  :			List[List[str]]			,				lowerCAmelCase  :			int = 1			,				lowerCAmelCase  :			int = 4			,				)	->				Dict[str, float]:




         """simple docstring"""

         return {
             "google_bleu": gleu_score.corpus_gleu(
                 list_of_references=lowerCAmelCase			,				hypotheses=lowerCAmelCase			,				min_len=lowerCAmelCase			,				max_len=lowerCAmelCase)
         }
