






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__a      :       Dict				  =   version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
  from nltk import word_tokenize


__a      :       List[str]				  =   "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

__a      :       Dict				  =   "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

__a      :       Optional[int]				  =   "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"


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







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         return datasets.MetricInfo(
             description=_DESCRIPTION ,	citation=_CITATION ,	inputs_description=_KWARGS_DESCRIPTION ,	features=datasets.Features(
                 {
                     """predictions""": datasets.Value("""string""" ,	id="""sequence"""				),
                     """references""": datasets.Value("""string""" ,	id="""sequence"""				),
                 }				) ,	codebase_urls=["""https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""] ,	reference_urls=[
                 """https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
                 """https://en.wikipedia.org/wiki/METEOR""",
             ] ,	)




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



         import nltk

         nltk.download("""wordnet"""				)
         if NLTK_VERSION >= version.Version("""3.6.5"""				):
             nltk.download("""punkt"""				)
         if NLTK_VERSION >= version.Version("""3.6.6"""				):
             nltk.download("""omw-1.4"""				)





     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Tuple=0.9 ,	UpperCamelCase_       :			Dict=3 ,	UpperCamelCase_       :			Any=0.5				):


         """simple docstring"""



         if NLTK_VERSION >= version.Version("""3.6.5"""				):
             __A         =	[
                 meteor_score.single_meteor_score(
                     word_tokenize(UpperCamelCase_				) ,	word_tokenize(UpperCamelCase_				) ,	alpha=UpperCamelCase_ ,	beta=UpperCamelCase_ ,	gamma=UpperCamelCase_				)
                 for ref, pred in zip(UpperCamelCase_ ,	UpperCamelCase_				)
             ]
         else:
             __A         =	[
                 meteor_score.single_meteor_score(UpperCamelCase_ ,	UpperCamelCase_ ,	alpha=UpperCamelCase_ ,	beta=UpperCamelCase_ ,	gamma=UpperCamelCase_				)
                 for ref, pred in zip(UpperCamelCase_ ,	UpperCamelCase_				)
             ]

         return {"meteor": np.mean(UpperCamelCase_				)}












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      :       List[Any]				  =   logging.get_logger(__name__)

__a      :       List[str]				  =   {
    "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       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"gpt_neo"
     SCREAMING_SNAKE_CASE   						=				["past_key_values"]
     SCREAMING_SNAKE_CASE   						=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




     def __init__(				self       :			Optional[Any] ,	UpperCamelCase_       :			List[Any]=50_257 ,	UpperCamelCase_       :			Union[str, Any]=2_048 ,	UpperCamelCase_       :			List[str]=2_048 ,	UpperCamelCase_       :			Optional[int]=24 ,	UpperCamelCase_       :			Any=[[["global", "local"], 12]] ,	UpperCamelCase_       :			Tuple=16 ,	UpperCamelCase_       :			List[Any]=None ,	UpperCamelCase_       :			Optional[Any]=256 ,	UpperCamelCase_       :			List[str]="gelu_new" ,	UpperCamelCase_       :			str=0.0 ,	UpperCamelCase_       :			Optional[Any]=0.0 ,	UpperCamelCase_       :			str=0.0 ,	UpperCamelCase_       :			List[Any]=0.1 ,	UpperCamelCase_       :			str=1e-5 ,	UpperCamelCase_       :			Optional[Any]=0.02 ,	UpperCamelCase_       :			Union[str, Any]=True ,	UpperCamelCase_       :			Union[str, Any]=50_256 ,	UpperCamelCase_       :			Dict=50_256 ,	**UpperCamelCase_       :			Union[str, Any] ,	):


         """simple docstring"""



         __A         =	vocab_size
         __A         =	max_position_embeddings
         __A         =	hidden_size
         __A         =	num_layers
         __A         =	num_heads
         __A         =	intermediate_size
         __A         =	window_size
         __A         =	activation_function
         __A         =	resid_dropout
         __A         =	embed_dropout
         __A         =	attention_dropout
         __A         =	classifier_dropout
         __A         =	layer_norm_epsilon
         __A         =	initializer_range
         __A         =	use_cache

         __A         =	bos_token_id
         __A         =	eos_token_id

         __A         =	attention_types
         __A         =	self.expand_attention_types_params(UpperCamelCase_				)

         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=UpperCamelCase_ ,	eos_token_id=UpperCamelCase_ ,	**UpperCamelCase_				)





     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			str				):


         """simple docstring"""



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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Any			,				__lowercase   :			int			,				__lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    import torch

    __A         =	input.size()
    __A         =	len(__lowercase    )
    __A         =	shape[dimension]

    __A         =	torch.arange(0			,				__lowercase			,				__lowercase    )
    __A         =	torch.div(sizedim - size			,				__lowercase			,				rounding_mode="""floor"""    ) + 1
    __A         =	torch.arange(__lowercase    ) + low_indices[:min_length][:, None]

    __A         =	[slice(__lowercase    )] * rank
    __A         =	indices
    __A         =	input[s]

    __A         =	list(range(0			,				rank + 1    )    )
    perm.append(perm.pop(dimension + 1    )    )

    return sliced.permute(__lowercase    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Any    )						->      Union[str, Any]:





    """simple docstring"""

    import torch

    __A         =	torch.arange(1			,				__lowercase    )
    __A         =	torch.remainder(__lowercase			,				__lowercase    )
    __A         =	remainders == 0
    __A         =	candidates[divisor_indices]
    __A         =	torch.max(__lowercase    )
    return largest_divisor, torch.div(__lowercase			,				__lowercase			,				rounding_mode="""floor"""    )






class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     @property
     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
         if self.use_past:
             self.fill_with_past_key_values_(UpperCamelCase_ ,	direction="""inputs"""				)
             __A         =	{0: """batch""", 1: """past_sequence + sequence"""}
         else:
             __A         =	{0: """batch""", 1: """sequence"""}

         return common_inputs




     @property
     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         return self._config.num_heads




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			PreTrainedTokenizer ,	UpperCamelCase_       :			int = -1 ,	UpperCamelCase_       :			int = -1 ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			Optional[TensorType] = None ,	):


         """simple docstring"""



         __A         =	super(UpperCamelCase_ ,	self				).generate_dummy_inputs(
             UpperCamelCase_ ,	batch_size=UpperCamelCase_ ,	seq_length=UpperCamelCase_ ,	is_pair=UpperCamelCase_ ,	framework=UpperCamelCase_				)

         # We need to order the input in the way they appears in the forward()
         __A         =	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

                 __A    ,	__A         =	common_inputs["""input_ids"""].shape
                 # Not using the same length for past_key_values
                 __A         =	seqlen + 2
                 __A         =	(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 __A         =	[
                     (torch.zeros(UpperCamelCase_				), torch.zeros(UpperCamelCase_				)) for _ in range(self.num_layers				)
                 ]

         __A         =	common_inputs["""attention_mask"""]
         if self.use_past:
             __A         =	ordered_inputs["""attention_mask"""].dtype
             __A         =	torch.cat(
                 [ordered_inputs["""attention_mask"""], torch.ones(UpperCamelCase_ ,	UpperCamelCase_ ,	dtype=UpperCamelCase_				)] ,	dim=1				)

         return ordered_inputs





     @property
     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         return 13




