


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[str]  				=			'''bloom'''
							_lowercase     :      Tuple  				=			['''past_key_values''']
							_lowercase     :      Tuple  				=			{
							    '''num_hidden_layers''': '''n_layer''',
							    '''num_attention_heads''': '''n_head''',
							}

							def __init__(							self:     Optional[int]   ,						UpperCamelCase_:     Dict=250_880   ,						UpperCamelCase_:     str=64   ,						UpperCamelCase_:     Union[str, Any]=2   ,						UpperCamelCase_:     List[Any]=8   ,						UpperCamelCase_:     int=1E-5   ,						UpperCamelCase_:     List[str]=0.02   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     Optional[int]=1   ,						UpperCamelCase_:     str=2   ,						UpperCamelCase_:     int=False   ,						UpperCamelCase_:     Tuple=0.0   ,						UpperCamelCase_:     Optional[Any]=0.0   ,						UpperCamelCase_:     List[str]=1   ,						UpperCamelCase_:     Optional[Any]=False   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				vocab_size
									# Backward compatibility with n_embed kwarg
									lowercase__						=				kwargs.pop('''n_embed'''   ,						UpperCamelCase_							)
									lowercase__						=				hidden_size if n_embed is None else n_embed
									lowercase__						=				n_layer
									lowercase__						=				n_head
									lowercase__						=				layer_norm_epsilon
									lowercase__						=				initializer_range
									lowercase__						=				use_cache
									lowercase__						=				pretraining_tp
									lowercase__						=				apply_residual_connection_post_layernorm
									lowercase__						=				hidden_dropout
									lowercase__						=				attention_dropout

									lowercase__						=				bos_token_id
									lowercase__						=				eos_token_id
									lowercase__						=				slow_but_exact

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


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			version.parse('''1.12'''      )

							def __init__(							self:     Optional[int]   ,						UpperCamelCase_:     PretrainedConfig   ,						UpperCamelCase_:     str = "default"   ,						UpperCamelCase_:     List[PatchingSpec] = None   ,						UpperCamelCase_:     bool = False   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									super().__init__(UpperCamelCase_   ,						task=UpperCamelCase_   ,						patching_specs=UpperCamelCase_   ,						use_past=UpperCamelCase_							)
									if not getattr(self._config   ,						'''pad_token_id'''   ,						UpperCamelCase_							):
											# TODO: how to do that better?
											lowercase__						=				0

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





									"""simple docstring"""







									lowercase__						=				OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}							)
									if self.use_past:
											# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
											self.fill_with_past_key_values_(UpperCamelCase_   ,						direction='''inputs'''   ,						inverted_values_shape=UpperCamelCase_							)
											lowercase__						=				{0: '''batch''', 1: '''past_sequence + sequence'''}
									else:
											lowercase__						=				{0: '''batch''', 1: '''sequence'''}

									return common_inputs

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       int:





									"""simple docstring"""







									return self._config.n_layer

							@property
							def 				lowerCamelCase_			(							self:     Any							)      ->       int:





									"""simple docstring"""







									return self._config.n_head

							@property
							def 				lowerCamelCase_			(							self:     Any							)      ->       float:





									"""simple docstring"""







									return 1E-3

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     "PreTrainedTokenizer"   ,						UpperCamelCase_:     int = -1   ,						UpperCamelCase_:     int = -1   ,						UpperCamelCase_:     bool = False   ,						UpperCamelCase_:     Optional["TensorType"] = None   ,						)      ->       Mapping[str, Any]:





									"""simple docstring"""







									lowercase__						=				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()
									lowercase__						=				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

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

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

									return ordered_inputs


							@property
							def 				lowerCamelCase_			(							self:     Any							)      ->       int:





									"""simple docstring"""







									return 13






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase	=							version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
			from nltk import word_tokenize


lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\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 	_a							(      datasets.Metric      ):

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""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:     Union[str, Any]   ,						UpperCamelCase_:     List[str]							)      ->       Union[str, Any]:





									"""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:     Any   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict=0.9   ,						UpperCamelCase_:     Union[str, Any]=3   ,						UpperCamelCase_:     Optional[int]=0.5							)      ->       Dict:





									"""simple docstring"""







									if NLTK_VERSION >= version.Version('''3.6.5'''							):
											lowercase__						=				[
											    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:
											lowercase__						=				[
											    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_							)}


