



"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


A:     Optional[int]										=	logging.get_logger(__name__)

A:     Union[str, Any]										=	{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     List[str]		=		'gpt_neo'
   __lowerCAmelCase						:     Any		=		['past_key_values']
   __lowerCAmelCase						:     int		=		{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=50257   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=24   ,  _SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]   ,  _SCREAMING_SNAKE_CASE=16   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE="gelu_new"   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=1E-5   ,  _SCREAMING_SNAKE_CASE=0.02   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=50256   ,  _SCREAMING_SNAKE_CASE=50256   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			vocab_size
    UpperCAmelCase			:      List[Any]			=			max_position_embeddings
    UpperCAmelCase			:      str			=			hidden_size
    UpperCAmelCase			:      Dict			=			num_layers
    UpperCAmelCase			:      List[Any]			=			num_heads
    UpperCAmelCase			:      Optional[Any]			=			intermediate_size
    UpperCAmelCase			:      Dict			=			window_size
    UpperCAmelCase			:      int			=			activation_function
    UpperCAmelCase			:      Tuple			=			resid_dropout
    UpperCAmelCase			:      int			=			embed_dropout
    UpperCAmelCase			:      Optional[int]			=			attention_dropout
    UpperCAmelCase			:      Optional[int]			=			classifier_dropout
    UpperCAmelCase			:      Any			=			layer_norm_epsilon
    UpperCAmelCase			:      Union[str, Any]			=			initializer_range
    UpperCAmelCase			:      List[str]			=			use_cache

    UpperCAmelCase			:      Union[str, Any]			=			bos_token_id
    UpperCAmelCase			:      str			=			eos_token_id

    UpperCAmelCase			:      Union[str, Any]			=			attention_types
    UpperCAmelCase			:      int			=			self.expand_attention_types_params(_SCREAMING_SNAKE_CASE		)

    if len(self.attention_layers		) != self.num_layers:
     raise ValueError(
         """Configuration for convolutional module is incorrect. """
         """It is required that `len(config.attention_layers)` == `config.num_layers` """
         F"but is `len(config.attention_layers) = {len(self.attention_layers		)}`, "
         F"`config.num_layers = {self.num_layers}`. "
         """`config.attention_layers` is prepared using `config.attention_types`. """
         """Please verify the value of `config.attention_types` argument."""		)

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




   @staticmethod
   def 			SCREAMING_SNAKE_CASE  (     _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			[]
    for item in attention_types:
     for _ in range(item[1]		):
      attentions.extend(item[0]		)
    return attentions

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	Dict		)     ->   List[Any]:
 import torch

 UpperCAmelCase			:      Optional[Any]			=			input.size()
 UpperCAmelCase			:      int			=			len(UpperCamelCase		)
 UpperCAmelCase			:      Optional[Any]			=			shape[dimension]

 UpperCAmelCase			:      Union[str, Any]			=			torch.arange(0    ,	UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			torch.div(sizedim - size    ,	UpperCamelCase    ,	rounding_mode="""floor"""		) + 1
 UpperCAmelCase			:      Optional[Any]			=			torch.arange(UpperCamelCase		) + low_indices[:min_length][:, None]

 UpperCAmelCase			:      List[Any]			=			[slice(UpperCamelCase		)] * rank
 UpperCAmelCase			:      Optional[Any]			=			indices
 UpperCAmelCase			:      Optional[Any]			=			input[s]

 UpperCAmelCase			:      List[Any]			=			list(range(0    ,	rank + 1		)		)
 perm.append(perm.pop(dimension + 1		)		)

 return sliced.permute(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Dict		)     ->   Dict:
 import torch

 UpperCAmelCase			:      List[str]			=			torch.arange(1    ,	UpperCamelCase		)
 UpperCAmelCase			:      Any			=			torch.remainder(UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      Union[str, Any]			=			remainders == 0
 UpperCAmelCase			:      str			=			candidates[divisor_indices]
 UpperCAmelCase			:      Optional[int]			=			torch.max(UpperCamelCase		)
 return largest_divisor, torch.div(UpperCamelCase    ,	UpperCamelCase    ,	rounding_mode="""floor"""		)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}		)
    if self.use_past:
     self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE   ,  direction="""inputs"""		)
     UpperCAmelCase			:      str			=			{0: """batch""", 1: """past_sequence + sequence"""}
    else:
     UpperCAmelCase			:      List[str]			=			{0: """batch""", 1: """sequence"""}

    return common_inputs

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return self._config.num_heads

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = -1   ,  _SCREAMING_SNAKE_CASE = -1   ,  _SCREAMING_SNAKE_CASE = False   ,  _SCREAMING_SNAKE_CASE = None   ,  )						->   Mapping[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			super(_SCREAMING_SNAKE_CASE   ,  self		).generate_dummy_inputs(
        _SCREAMING_SNAKE_CASE   ,  batch_size=_SCREAMING_SNAKE_CASE   ,  seq_length=_SCREAMING_SNAKE_CASE   ,  is_pair=_SCREAMING_SNAKE_CASE   ,  framework=_SCREAMING_SNAKE_CASE		)

    # We need to order the input in the way they appears in the forward()
    UpperCAmelCase			:      Dict			=			OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}		)

    # Need to add the past_keys
    if self.use_past:
     if not is_torch_available():
      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
     else:
      import torch

      UpperCAmelCase			:      List[Any]			=			common_inputs["""input_ids"""].shape
      # Not using the same length for past_key_values
      UpperCAmelCase			:      Any			=			seqlen + 2
      UpperCAmelCase			:      str			=			(
          batch,
          self.num_attention_heads,
          past_key_values_length,
          self._config.hidden_size // self.num_attention_heads,
      )
      UpperCAmelCase			:      Optional[int]			=			[
          (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(self.num_layers		)
      ]

    UpperCAmelCase			:      List[str]			=			common_inputs["""attention_mask"""]
    if self.use_past:
     UpperCAmelCase			:      str			=			ordered_inputs["""attention_mask"""].dtype
     UpperCAmelCase			:      int			=			torch.cat(
         [ordered_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  dtype=_SCREAMING_SNAKE_CASE		)]   ,  dim=1		)

    return ordered_inputs




   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return 13





"""simple docstring"""
import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


A:     Optional[Any]										=	"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

A:     Optional[int]										=	"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

A:     Optional[int]										=	"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



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

   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"""		),
            }		)   ,  codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,  reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,  )




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=False		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			compute_bleu(
        reference_corpus=_SCREAMING_SNAKE_CASE   ,  translation_corpus=_SCREAMING_SNAKE_CASE   ,  max_order=_SCREAMING_SNAKE_CASE   ,  smooth=_SCREAMING_SNAKE_CASE		)
    ((UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase))			:      Dict			=			score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
