





"""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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    '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_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""gpt_neo"""
				__snake_case   :       Optional[int]	 =		["""past_key_values"""]
				__snake_case   :       Optional[int]	 =		{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




				def __init__(     self  :Any						,    __lowercase  :Optional[Any]=5_0257						,    __lowercase  :Tuple=2048						,    __lowercase  :Optional[Any]=2048						,    __lowercase  :Any=24						,    __lowercase  :Optional[Any]=[[["global", "local"], 12]]						,    __lowercase  :List[Any]=16						,    __lowercase  :Optional[int]=None						,    __lowercase  :List[str]=256						,    __lowercase  :List[str]="gelu_new"						,    __lowercase  :Union[str, Any]=0.0						,    __lowercase  :str=0.0						,    __lowercase  :int=0.0						,    __lowercase  :List[str]=0.1						,    __lowercase  :Union[str, Any]=1e-5						,    __lowercase  :List[Any]=0.02						,    __lowercase  :Dict=True						,    __lowercase  :str=5_0256						,    __lowercase  :str=5_0256						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Dict         =vocab_size
											__lowerCamelCase      :      Tuple         =max_position_embeddings
											__lowerCamelCase      :      Dict         =hidden_size
											__lowerCamelCase      :      Optional[Any]         =num_layers
											__lowerCamelCase      :      str         =num_heads
											__lowerCamelCase      :      Dict         =intermediate_size
											__lowerCamelCase      :      List[Any]         =window_size
											__lowerCamelCase      :      int         =activation_function
											__lowerCamelCase      :      Any         =resid_dropout
											__lowerCamelCase      :      str         =embed_dropout
											__lowerCamelCase      :      Optional[Any]         =attention_dropout
											__lowerCamelCase      :      Optional[Any]         =classifier_dropout
											__lowerCamelCase      :      Optional[Any]         =layer_norm_epsilon
											__lowerCamelCase      :      Union[str, Any]         =initializer_range
											__lowerCamelCase      :      Dict         =use_cache

											__lowerCamelCase      :      List[Any]         =bos_token_id
											__lowerCamelCase      :      Any         =eos_token_id

											__lowerCamelCase      :      str         =attention_types
											__lowerCamelCase      :      int         =self.expand_attention_types_params(__lowercase			)

											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=__lowercase						,    eos_token_id=__lowercase						,    **__lowercase			)




				@staticmethod
				def 					__lowercase  (     __lowercase  :Optional[int]			):
											__lowerCamelCase      :      List[Any]         =[]
											for item in attention_types:
																		for _ in range(item[1]			):
																									attentions.extend(item[0]			)
											return attentions



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							import torch

							__lowerCamelCase      :      Optional[Any]         =input.size()
							__lowerCamelCase      :      Tuple         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      List[Any]         =shape[dimension]

							__lowerCamelCase      :      Optional[Any]         =torch.arange(0    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =torch.div(sizedim - size    ,						SCREAMING_SNAKE_CASE    ,						rounding_mode='''floor'''   ) + 1
							__lowerCamelCase      :      Tuple         =torch.arange(SCREAMING_SNAKE_CASE   ) + low_indices[:min_length][:, None]

							__lowerCamelCase      :      int         =[slice(SCREAMING_SNAKE_CASE   )] * rank
							__lowerCamelCase      :      List[Any]         =indices
							__lowerCamelCase      :      Dict         =input[s]

							__lowerCamelCase      :      str         =list(range(0    ,						rank + 1   )   )
							perm.append(perm.pop(dimension + 1   )   )

							return sliced.permute(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							import torch

							__lowerCamelCase      :      Tuple         =torch.arange(1    ,						SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =torch.remainder(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Any         =remainders == 0
							__lowerCamelCase      :      Optional[int]         =candidates[divisor_indices]
							__lowerCamelCase      :      List[str]         =torch.max(SCREAMING_SNAKE_CASE   )
							return largest_divisor, torch.div(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						rounding_mode='''floor'''   )
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				@property
				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      List[str]         =OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
											if self.use_past:
																		self.fill_with_past_key_values_(__lowercase						,    direction='''inputs'''			)
																		__lowerCamelCase      :      str         ={0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
																		__lowerCamelCase      :      Union[str, Any]         ={0: '''batch''', 1: '''sequence'''}

											return common_inputs




				@property
				def 					__lowercase  (     self  :List[Any]			):
											return self._config.num_heads




				def 					__lowercase  (     self  :Any						,    __lowercase  :PreTrainedTokenizer						,    __lowercase  :int = -1						,    __lowercase  :int = -1						,    __lowercase  :bool = False						,    __lowercase  :Optional[TensorType] = None						,    ):
											__lowerCamelCase      :      Dict         =super(__lowercase						,    self			).generate_dummy_inputs(
											    __lowercase						,    batch_size=__lowercase						,    seq_length=__lowercase						,    is_pair=__lowercase						,    framework=__lowercase			)

											# We need to order the input in the way they appears in the forward()
											__lowerCamelCase      :      Optional[int]         =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

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

											__lowerCamelCase      :      Optional[Any]         =common_inputs['''attention_mask''']
											if self.use_past:
																		__lowerCamelCase      :      str         =ordered_inputs['''attention_mask'''].dtype
																		__lowerCamelCase      :      Any         =torch.cat(
																		    [ordered_inputs['''attention_mask'''], torch.ones(__lowercase						,    __lowercase						,    dtype=__lowercase			)]						,    dim=1			)

											return ordered_inputs




				@property
				def 					__lowercase  (     self  :Tuple			):
											return 13











"""simple docstring"""





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCamelCase							=      '\\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'

_UpperCamelCase							=      '\\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'

_UpperCamelCase							=      '\\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 		SCREAMING_SNAKE_CASE_							(    datasets.Metric   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Optional[Any]			):
											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 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :List[List[List[str]]]						,    __lowercase  :List[List[str]]						,    __lowercase  :int = 1						,    __lowercase  :int = 4						,    ):
											return {
											    "google_bleu": gleu_score.corpus_gleu(
											        list_of_references=__lowercase						,    hypotheses=__lowercase						,    min_len=__lowercase						,    max_len=__lowercase			)
											}




