

"""simple docstring"""



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


SCREAMING_SNAKE_CASE_    :		Optional[Any]							     =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    :		Dict							     =  {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}



class     a  (				_lowerCAmelCase ):



    """simple docstring"""



    UpperCAmelCase         =    "switch_transformers"
    UpperCAmelCase         =    ["past_key_values"]
    UpperCAmelCase         =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(   self:       Tuple			,  UpperCamelCase:       Optional[Any]=3_21_28			,  UpperCamelCase:       str=7_68			,  UpperCamelCase:       Optional[int]=64			,  UpperCamelCase:       Optional[Any]=20_48			,  UpperCamelCase:       str=64			,  UpperCamelCase:       Dict=12			,  UpperCamelCase:       Tuple=3			,  UpperCamelCase:       Optional[Any]=12			,  UpperCamelCase:       int=3			,  UpperCamelCase:       Union[str, Any]=12			,  UpperCamelCase:       Tuple=8			,  UpperCamelCase:       int=False			,  UpperCamelCase:       List[Any]=0.01			,  UpperCamelCase:       Optional[int]="float32"			,  UpperCamelCase:       Dict=False			,  UpperCamelCase:       Dict=32			,  UpperCamelCase:       Any=1_28			,  UpperCamelCase:       List[Any]=0.1			,  UpperCamelCase:       Tuple=1e-6			,  UpperCamelCase:       Tuple=0.001			,  UpperCamelCase:       Union[str, Any]=0.001			,  UpperCamelCase:       Dict=1.0			,  UpperCamelCase:       Dict="relu"			,  UpperCamelCase:       Any=True			,  UpperCamelCase:       Any=False			,  UpperCamelCase:       Tuple=True			,  UpperCamelCase:       int=0			,  UpperCamelCase:       Any=1			,  **UpperCamelCase:       Any			,  ):






         """simple docstring"""


         A__								=     vocab_size
         A__								=     d_model
         A__								=     d_kv
         A__								=     d_ff

         A__								=     num_sparse_encoder_layers

         A__								=     num_layers
         A__								=     (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A__								=     num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
              A__								=     self.num_layers // self.num_sparse_encoder_layers
         else:
              A__								=     self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
              A__								=     self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              A__								=     self.num_decoder_layers  # HACK: this will create 0 sparse layers

         A__								=     num_heads
         A__								=     num_experts
         A__								=     expert_capacity
         A__								=     router_bias
         A__								=     router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}"""				)
         A__								=     router_dtype

         A__								=     router_ignore_padding_tokens
         A__								=     relative_attention_num_buckets
         A__								=     relative_attention_max_distance

         A__								=     dropout_rate
         A__								=     layer_norm_epsilon
         A__								=     initializer_factor
         A__								=     feed_forward_proj
         A__								=     use_cache
         A__								=     add_router_probs

         A__								=     router_z_loss_coef
         A__								=     router_aux_loss_coef

         A__								=     self.feed_forward_proj.split("""-"""				)
         A__								=     act_info[-1]
         A__								=     act_info[0] == """gated"""

         if len(_lowerCamelCase				) > 1 and act_info[0] != "gated" or len(_lowerCamelCase				) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                  """\'gated-gelu\' or \'relu\'"""				)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              A__								=     """gelu_new"""

         super().__init__(
             pad_token_id=_lowerCamelCase			,  eos_token_id=_lowerCamelCase			,  is_encoder_decoder=_lowerCamelCase			,  **_lowerCamelCase			,  )














"""simple docstring"""


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowercase_				     =     '\\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'

lowercase_				     =     '\\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'

lowercase_				     =     '\\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 					snake_case		(							datasets.Metric						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''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 :     str,       _lowerCamelCase :     List[List[List[str]]],       _lowerCamelCase :     List[List[str]],       _lowerCamelCase :     int = 1,       _lowerCamelCase :     int = 4,       ):

     '''simple docstring'''
     return {
         "google_bleu": gleu_score.corpus_gleu(
             list_of_references=_lowerCamelCase,       hypotheses=_lowerCamelCase,       min_len=_lowerCamelCase,       max_len=_lowerCamelCase  )
     }




