





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class 	UpperCamelCase__				(   UpperCamelCase__							,   UpperCamelCase__     ):



     '''simple docstring'''

     @register_to_config
     def __init__(							self		:					Any		,lowerCamelCase__		:					int = 128		,lowerCamelCase__		:					int = 256		,lowerCamelCase__		:					Tuple = 2000.0		,lowerCamelCase__		:					str = 768		,lowerCamelCase__		:					List[str] = 12		,lowerCamelCase__		:					int = 12		,lowerCamelCase__		:					Union[str, Any] = 64		,lowerCamelCase__		:					Optional[Any] = 2048		,lowerCamelCase__		:					Tuple = 0.1		,)  ->	Tuple:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE	=		nn.Sequential(
               nn.Linear(_a		,d_model * 4		,bias=_a )		,nn.SiLU()		,nn.Linear(d_model * 4		,d_model * 4		,bias=_a )		,nn.SiLU()		,)

           SCREAMING_SNAKE_CASE	=		nn.Embedding(_a		,_a )
           SCREAMING_SNAKE_CASE	=		False

           SCREAMING_SNAKE_CASE	=		nn.Linear(_a		,_a		,bias=_a )

           SCREAMING_SNAKE_CASE	=		nn.Dropout(p=_a )

           SCREAMING_SNAKE_CASE	=		nn.ModuleList()
           for lyr_num in range(_a ):
                 # FiLM conditional T5 decoder
                 SCREAMING_SNAKE_CASE	=		DecoderLayer(d_model=_a		,d_kv=_a		,num_heads=_a		,d_ff=_a		,dropout_rate=_a )
                 self.decoders.append(_a )

           SCREAMING_SNAKE_CASE	=		TaLayerNorm(_a )

           SCREAMING_SNAKE_CASE	=		nn.Dropout(p=_a )
           SCREAMING_SNAKE_CASE	=		nn.Linear(_a		,_a		,bias=_a )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		torch.mul(query_input.unsqueeze(-1 )		,key_input.unsqueeze(-2 ) )
           return mask.unsqueeze(-3 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		decoder_input_tokens.shape
           assert decoder_noise_time.shape == (batch,)

           # decoder_noise_time is in [0, 1), so rescale to expected timing range.
           SCREAMING_SNAKE_CASE	=		get_timestep_embedding(
               decoder_noise_time * self.config.max_decoder_noise_time		,embedding_dim=self.config.d_model		,max_period=self.config.max_decoder_noise_time		,).to(dtype=self.dtype )

           SCREAMING_SNAKE_CASE	=		self.conditioning_emb(_a ).unsqueeze(1 )

           assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

           SCREAMING_SNAKE_CASE	=		decoder_input_tokens.shape[1]

           # If we want to use relative positions for audio context, we can just offset
           # this sequence by the length of encodings_and_masks.
           SCREAMING_SNAKE_CASE	=		torch.broadcast_to(
               torch.arange(_a		,device=decoder_input_tokens.device )		,(batch, seq_length)		,)

           SCREAMING_SNAKE_CASE	=		self.position_encoding(_a )

           SCREAMING_SNAKE_CASE	=		self.continuous_inputs_projection(_a )
           inputs += position_encodings
           SCREAMING_SNAKE_CASE	=		self.dropout(_a )

           # decoder: No padding present.
           SCREAMING_SNAKE_CASE	=		torch.ones(
               decoder_input_tokens.shape[:2]		,device=decoder_input_tokens.device		,dtype=inputs.dtype )

           # Translate encoding masks to encoder-decoder masks.
           SCREAMING_SNAKE_CASE	=		[(x, self.encoder_decoder_mask(_a		,_a )) for x, y in encodings_and_masks]

           # cross attend style: concat encodings
           SCREAMING_SNAKE_CASE	=		torch.cat([x[0] for x in encodings_and_encdec_masks]		,dim=1 )
           SCREAMING_SNAKE_CASE	=		torch.cat([x[1] for x in encodings_and_encdec_masks]		,dim=-1 )

           for lyr in self.decoders:
                 SCREAMING_SNAKE_CASE	=		lyr(
                     _a		,conditioning_emb=_a		,encoder_hidden_states=_a		,encoder_attention_mask=_a		,)[0]

           SCREAMING_SNAKE_CASE	=		self.decoder_norm(_a )
           SCREAMING_SNAKE_CASE	=		self.post_dropout(_a )

           SCREAMING_SNAKE_CASE	=		self.spec_out(_a )
           return spec_out

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					str		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					int=1e-6 )  ->	List[Any]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		nn.ModuleList()

           # cond self attention: layer 0
           self.layer.append(
               TaLayerSelfAttentionCond(d_model=_a		,d_kv=_a		,num_heads=_a		,dropout_rate=_a ) )

           # cross attention: layer 1
           self.layer.append(
               TaLayerCrossAttention(
                   d_model=_a		,d_kv=_a		,num_heads=_a		,dropout_rate=_a		,layer_norm_epsilon=_a		,) )

           # Film Cond MLP + dropout: last layer
           self.layer.append(
               TaLayerFFCond(d_model=_a		,d_ff=_a		,dropout_rate=_a		,layer_norm_epsilon=_a ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					int=None		,lowerCamelCase__		:					Union[str, Any]=None		,lowerCamelCase__		:					Dict=None		,lowerCamelCase__		:					List[str]=None		,lowerCamelCase__		:					Tuple=None		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.layer[0](
               _a		,conditioning_emb=_a		,attention_mask=_a		,)

           if encoder_hidden_states is not None:
                 SCREAMING_SNAKE_CASE	=		torch.where(encoder_attention_mask > 0		,0		,-1e1_0 ).to(
                     encoder_hidden_states.dtype )

                 SCREAMING_SNAKE_CASE	=		self.layer[1](
                     _a		,key_value_states=_a		,attention_mask=_a		,)

           # Apply Film Conditional Feed Forward layer
           SCREAMING_SNAKE_CASE	=		self.layer[-1](_a		,_a )

           return (hidden_states,)

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					List[str]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Any )  ->	Tuple:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		TaLayerNorm(_a )
           SCREAMING_SNAKE_CASE	=		TaFiLMLayer(in_features=d_model * 4		,out_features=_a )
           SCREAMING_SNAKE_CASE	=		Attention(query_dim=_a		,heads=_a		,dim_head=_a		,out_bias=_a		,scale_qk=_a )
           SCREAMING_SNAKE_CASE	=		nn.Dropout(_a )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Dict=None		,lowerCamelCase__		:					List[str]=None		,)  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.layer_norm(_a )

           if conditioning_emb is not None:
                 SCREAMING_SNAKE_CASE	=		self.FiLMLayer(_a		,_a )

           # Self-attention block
           SCREAMING_SNAKE_CASE	=		self.attention(_a )

           SCREAMING_SNAKE_CASE	=		hidden_states + self.dropout(_a )

           return hidden_states

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Tuple		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Optional[Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					int )  ->	List[str]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		Attention(query_dim=_a		,heads=_a		,dim_head=_a		,out_bias=_a		,scale_qk=_a )
           SCREAMING_SNAKE_CASE	=		TaLayerNorm(_a		,eps=_a )
           SCREAMING_SNAKE_CASE	=		nn.Dropout(_a )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					List[Any]=None		,lowerCamelCase__		:					List[Any]=None		,)  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.layer_norm(_a )
           SCREAMING_SNAKE_CASE	=		self.attention(
               _a		,encoder_hidden_states=_a		,attention_mask=attention_mask.squeeze(1 )		,)
           SCREAMING_SNAKE_CASE	=		hidden_states + self.dropout(_a )
           return layer_output

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Any		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Optional[Any] )  ->	Dict:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		TaDenseGatedActDense(d_model=_a		,d_ff=_a		,dropout_rate=_a )
           SCREAMING_SNAKE_CASE	=		TaFiLMLayer(in_features=d_model * 4		,out_features=_a )
           SCREAMING_SNAKE_CASE	=		TaLayerNorm(_a		,eps=_a )
           SCREAMING_SNAKE_CASE	=		nn.Dropout(_a )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Optional[int]=None )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.layer_norm(_a )
           if conditioning_emb is not None:
                 SCREAMING_SNAKE_CASE	=		self.film(_a		,_a )

           SCREAMING_SNAKE_CASE	=		self.DenseReluDense(_a )
           SCREAMING_SNAKE_CASE	=		hidden_states + self.dropout(_a )
           return hidden_states

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					List[str]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any] )  ->	str:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		nn.Linear(_a		,_a		,bias=_a )
           SCREAMING_SNAKE_CASE	=		nn.Linear(_a		,_a		,bias=_a )
           SCREAMING_SNAKE_CASE	=		nn.Linear(_a		,_a		,bias=_a )
           SCREAMING_SNAKE_CASE	=		nn.Dropout(_a )
           SCREAMING_SNAKE_CASE	=		NewGELUActivation()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					str )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.act(self.wi_a(_a ) )
           SCREAMING_SNAKE_CASE	=		self.wi_a(_a )
           SCREAMING_SNAKE_CASE	=		hidden_gelu * hidden_linear
           SCREAMING_SNAKE_CASE	=		self.dropout(_a )

           SCREAMING_SNAKE_CASE	=		self.wo(_a )
           return hidden_states

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					int		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[str]=1e-6 )  ->	Union[str, Any]:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		nn.Parameter(torch.ones(_a ) )
           SCREAMING_SNAKE_CASE	=		eps






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					List[str] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		hidden_states.to(torch.floataa ).pow(2 ).mean(-1		,keepdim=_a )
           SCREAMING_SNAKE_CASE	=		hidden_states * torch.rsqrt(variance + self.variance_epsilon )

           # convert into half-precision if necessary
           if self.weight.dtype in [torch.floataa, torch.bfloataa]:
                 SCREAMING_SNAKE_CASE	=		hidden_states.to(self.weight.dtype )

           return self.weight * hidden_states

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Dict )  ->	torch.Tensor:




           '''simple docstring'''


           return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044715 * torch.pow(_a		,3.0 )) ))

class 	UpperCamelCase__				(   nn.Module     ):



     '''simple docstring'''

     def __init__(							self		:					Any		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Any )  ->	int:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		nn.Linear(_a		,out_features * 2		,bias=_a )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					int )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.scale_bias(_a )
           SCREAMING_SNAKE_CASE	=		torch.chunk(_a		,2		,-1 )
           SCREAMING_SNAKE_CASE	=		x * (1 + scale) + shift
           return x









from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


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

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

SCREAMING_SNAKE_CASE_		     = """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""

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



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	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        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,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__ )
           }


